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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

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Cited by:

  1. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
  2. Bjorkegren, Dan & Blumenstock, Joshua & Knight, Samsun, 2022. "(Machine) Learning What Policies Value," CEPR Discussion Papers 17364, C.E.P.R. Discussion Papers.
  3. Jiabei Yang & Issa J. Dahabreh & Jon A. Steingrimsson, 2022. "Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data," Biometrics, The International Biometric Society, vol. 78(2), pages 624-635, June.
  4. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
  5. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
  6. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  7. Holtrop, Niels & Wieringa, Jaap E., 2023. "Timing customer reactivation initiatives," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 570-589.
  8. Stephen Coussens & Jann Spiess, 2021. "Improving Inference from Simple Instruments through Compliance Estimation," Papers 2108.03726, arXiv.org.
  9. Jau-er Chen & Chen-Wei Hsiang, 2019. "Causal Random Forests Model Using Instrumental Variable Quantile Regression," Econometrics, MDPI, vol. 7(4), pages 1-22, December.
  10. Habimana, Dominique & Haughton, Jonathan & Nkurunziza, Joseph & Haughton, Dominique Marie-Annick, 2021. "Measuring the impact of unconditional cash transfers on consumption and poverty in Rwanda," World Development Perspectives, Elsevier, vol. 23(C).
  11. Denis Fougère & Nicolas Jacquemet, 2019. "Causal Inference and Impact Evaluation," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 510-511-5, pages 181-200.
  12. Hashibul Hassan & Asad Islam & Abu Siddique & Liang Choon Wang, 2024. "Telementoring and Homeschooling During School Closures: a Randomised Experiment in Rural Bangladesh," The Economic Journal, Royal Economic Society, vol. 134(662), pages 2418-2438.
  13. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Mar 2024.
  14. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
  15. Diogo G. C. Britto & Paolo Pinotti & Breno Sampaio, 2022. "The Effect of Job Loss and Unemployment Insurance on Crime in Brazil," Econometrica, Econometric Society, vol. 90(4), pages 1393-1423, July.
  16. Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
  17. Daniele Guariso, 2018. "Terrorist Attacks and Immigration Rhetoric: A Natural Experiment on British MPs," Working Paper Series 1218, Department of Economics, University of Sussex Business School.
  18. William Arbour, 2021. "Can Recidivism be Prevented from Behind Bars? Evidence from a Behavioral Program," Working Papers tecipa-683, University of Toronto, Department of Economics.
  19. Michela Bia & Martin Huber & Lukáš Lafférs, 2024. "Double Machine Learning for Sample Selection Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 958-969, July.
  20. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  21. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
  22. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
  23. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
  24. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
  25. Sandro Heiniger & Winfried Koeniger & Michael Lechner, 2022. "The Heterogeneous Response of Real Estate Asset Prices to a Global Shock," CESifo Working Paper Series 10083, CESifo.
  26. Hermes, Henning & Lergetporer, Philipp & Mierisch, Fabian & Schwerdt, Guido & Wiederhold, Simon, 2024. "Does Information about Inequality and Discrimination in Early Child Care Affect Policy Preferences?," IZA Discussion Papers 16759, Institute of Labor Economics (IZA).
  27. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
  28. Yamin Du & Huanhuan Cheng & Qing Liu & Song Tan, 2024. "The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
  29. Youmi Suk & Jee-Seon Kim & Hyunseung Kang, 2021. "Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 323-347, June.
  30. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
  31. Yiyan Huang & Cheuk Hang Leung & Siyi Wang & Yijun Li & Qi Wu, 2024. "Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators," Papers 2402.18392, arXiv.org, revised Oct 2024.
  32. Yamin Ahmad & Adam Check & Ming Chien Lo, 2024. "Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2139-2173, June.
  33. Jere R. Behrman & C. Simon Fan & Naijia Guo & Xiangdong Wei & Hongliang Zhang & Junsen Zhang, 2024. "Tutoring Efficacy, Household Substitution, And Student Achievement: Experimental Evidence From An After‐School Tutoring Program In Rural China," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(1), pages 149-189, February.
  34. Augustine Denteh & Helge Liebert, 2022. "Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment," Working Papers 2201, Tulane University, Department of Economics.
  35. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
  36. Evan T.R. Rosenman & Guillaume Basse & Art B. Owen & Mike Baiocchi, 2023. "Combining observational and experimental datasets using shrinkage estimators," Biometrics, The International Biometric Society, vol. 79(4), pages 2961-2973, December.
  37. Guihua Wang & Jun Li & Wallace J. Hopp, 2022. "An Instrumental Variable Forest Approach for Detecting Heterogeneous Treatment Effects in Observational Studies," Management Science, INFORMS, vol. 68(5), pages 3399-3418, May.
  38. Marianne Bertrand & Bruno Crépon & Alicia Marguerie & Patrick Premand, 2021. "Do Workfare Programs Live Up to Their Promises? Experimental Evidence from Cote D’Ivoire," NBER Working Papers 28664, National Bureau of Economic Research, Inc.
  39. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
  40. Hong Pan & Hanxun Zhou, 2020. "Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce," Electronic Commerce Research, Springer, vol. 20(2), pages 297-320, June.
  41. Uguccioni, James, 2022. "The long-run effects of parental unemployment in childhood," CLEF Working Paper Series 45, Canadian Labour Economics Forum (CLEF), University of Waterloo.
  42. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
  43. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
  44. Costanza Naguib, 2023. "Is the Impact of Opening the Borders Heterogeneous?," Diskussionsschriften dp2312, Universitaet Bern, Departement Volkswirtschaft.
  45. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Dec 2021.
  46. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  47. Christian Gische & Manuel C. Voelkle, 2022. "Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 868-901, September.
  48. Manuel Hermosilla, 2021. "Rushed Innovation: Evidence from Drug Licensing," Management Science, INFORMS, vol. 67(1), pages 257-278, January.
  49. Shi, Chengchun & Wan, Runzhe & Song, Ge & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2023. "A multiagent reinforcement learning framework for off-policy evaluation in two-sided markets," LSE Research Online Documents on Economics 117174, London School of Economics and Political Science, LSE Library.
  50. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
  51. Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024. "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, vol. 234(C).
  52. Edward McFowland III & Sriram Somanchi & Daniel B. Neill, 2018. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection," Papers 1803.09159, arXiv.org, revised May 2023.
  53. Miguel Godinho de Matos & Pedro Ferreira & Michael D. Smith, 2018. "The Effect of Subscription Video-on-Demand on Piracy: Evidence from a Household-Level Randomized Experiment," Management Science, INFORMS, vol. 64(12), pages 5610-5630, December.
  54. Federico Zincenko, 2023. "Nonparametric estimation of conditional densities by generalized random forests," Papers 2309.13251, arXiv.org, revised May 2024.
  55. Aysegül Kayaoglu & Ghassan Baliki & Tilman Brück & Melodie Al Daccache & Dorothee Weiffen, 2023. "How to conduct impact evaluations in humanitarian and conflict settings," HiCN Working Papers 387, Households in Conflict Network.
  56. Jonathan M.V. Davis & Sara B. Heller, 2020. "Rethinking the Benefits of Youth Employment Programs: The Heterogeneous Effects of Summer Jobs," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 664-677, October.
  57. Pons Rotger, Gabriel & Rosholm, Michael, 2020. "The Role of Beliefs in Long Sickness Absence: Experimental Evidence from a Psychological Intervention," IZA Discussion Papers 13582, Institute of Labor Economics (IZA).
  58. Zhexiao Lin & Fang Han, 2022. "On regression-adjusted imputation estimators of the average treatment effect," Papers 2212.05424, arXiv.org, revised Jan 2023.
  59. Carlos Fernández-Loría & Foster Provost & Jesse Anderton & Benjamin Carterette & Praveen Chandar, 2023. "A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation," Information Systems Research, INFORMS, vol. 34(2), pages 786-803, June.
  60. Axenbeck, Janna & Berner, Anne & Kneib, Thomas, 2022. "What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity," ZEW Discussion Papers 22-059, ZEW - Leibniz Centre for European Economic Research.
  61. Rametta, Jack T., 2024. "Did the Republican Revolution Hamstring Congressional Oversight? Evidence from 55,000 GAO Reports," OSF Preprints 7zk4p, Center for Open Science.
  62. O'Neill, E. & Weeks, M., 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Cambridge Working Papers in Economics 1865, Faculty of Economics, University of Cambridge.
  63. Seojeong Lee & Youngki Shin, 2018. "Optimal Estimation with Complete Subsets of Instruments," Department of Economics Working Papers 2018-15, McMaster University.
  64. Isaiah Hull & Anna Grodecka-Messi, 2022. "Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach," Papers 2203.14751, arXiv.org.
  65. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
  66. Jeremy Bertomeu, 2020. "Machine learning improves accounting: discussion, implementation and research opportunities," Review of Accounting Studies, Springer, vol. 25(3), pages 1135-1155, September.
  67. Miguel Godinho de Matos & Idris Adjerid, 2022. "Consumer Consent and Firm Targeting After GDPR: The Case of a Large Telecom Provider," Management Science, INFORMS, vol. 68(5), pages 3330-3378, May.
  68. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
  69. Patrick Dylong & Silke Übelmesser, 2024. "Vorbehalte gegenüber Zuwanderung: Die Rolle von Kontakten und Informationen," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 31(01), pages 17-23, February.
  70. Cordier, J.; & Salvi, I.; & Steinbeck, V.; & Geissler, A.; & Vogel, J.;, 2023. "Is rapid recovery always the best recovery? - Developing a machine learning approach for optimal assignment rules under capacity constraints for knee replacement patients," Health, Econometrics and Data Group (HEDG) Working Papers 23/08, HEDG, c/o Department of Economics, University of York.
  71. Christian Stetter & Philipp Mennig & Johannes Sauer, 2022. "Using Machine Learning to Identify Heterogeneous Impacts of Agri-Environment Schemes in the EU: A Case Study," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(4), pages 723-759.
  72. Niwen Zhou & Xu Guo & Lixing Zhu, 2022. "The role of propensity score structure in asymptotic efficiency of estimated conditional quantile treatment effect," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 718-743, June.
  73. Jan-Emmanuel De Neve & Clément Imbert & Johannes Spinnewijn & Teodora Tsankova & Maarten Luts, 2021. "How to Improve Tax Compliance? Evidence from Population-Wide Experiments in Belgium," Journal of Political Economy, University of Chicago Press, vol. 129(5), pages 1425-1463.
  74. Carlana, Michela & La Ferrara, Eliana, 2021. "Apart but Connected: Online Tutoring and Student Outcomes during the COVID-19 Pandemic," IZA Discussion Papers 14094, Institute of Labor Economics (IZA).
  75. Laura Giuliano, 2022. "A Comment on: “Goals and Gaps: Educational Careers of Immigrant Children” by Michela Carlana, Eliana La Ferrara, Paolo Pinotti," Econometrica, Econometric Society, vol. 90(1), pages 39-42, January.
  76. Newham, Melissa & Valente, Marica, 2024. "The cost of influence: How gifts to physicians shape prescriptions and drug costs," Journal of Health Economics, Elsevier, vol. 95(C).
  77. Hang Miao & Kui Zhao & Zhun Wang & Linbo Jiang & Quanhui Jia & Yanming Fang & Quan Yu, 2020. "Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference," Papers 2007.05188, arXiv.org.
  78. Kevin Blattler & Hannes Wallimann & Widar von Arx, 2024. "Free public transport to the destination: A causal analysis of tourists' travel mode choice," Papers 2401.14945, arXiv.org, revised Feb 2024.
  79. Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
  80. Cevat Giray Aksoy & Antonio Cabrales & Mathias Dolls & Ruben Durante & Lisa Windsteiger, 2021. "Calamities, Common Interests, Shared Identity: What Shapes Altruism and Reciprocity?," EconPol Working Paper 64, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  81. Mattos, Enlinson & Bressan, Rafael, 2022. "Nontariff barriers, trading companies and customs duties evasion," Textos para discussão 560, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
  82. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
  83. Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024. "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 636-669.
  84. Haaland, Ingar & Roth, Christopher, 2020. "Labor market concerns and support for immigration," Journal of Public Economics, Elsevier, vol. 191(C).
  85. A Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Osman Shami & Alexander Teytelboym, 2024. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," Journal of the European Economic Association, European Economic Association, vol. 22(2), pages 781-836.
  86. Guido Imbens & Yiqing Xu, 2024. "LaLonde (1986) after Nearly Four Decades: Lessons Learned," Papers 2406.00827, arXiv.org, revised Jun 2024.
  87. Boyuan Zhang, 2022. "Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models," Papers 2211.16714, arXiv.org, revised Oct 2023.
  88. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
  89. Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E & Wiemann, Thomas, 2024. "Model Averaging and Double Machine Learning," IZA Discussion Papers 16714, Institute of Labor Economics (IZA).
  90. Grodecka, Anna & Hull, Isaiah, 2019. "The Impact of Local Taxes and Public Services on Property Values," Working Paper Series 374, Sveriges Riksbank (Central Bank of Sweden).
  91. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
  92. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
  93. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," Papers 1712.04802, arXiv.org, revised Oct 2023.
  94. Ruyi Ge & Zhiqiang (Eric) Zheng & Xuan Tian & Li Liao, 2021. "Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending," Information Systems Research, INFORMS, vol. 32(3), pages 774-785, September.
  95. Thomas Lindner & Jonas Puck & Alain Verbeke, 2022. "Beyond addressing multicollinearity: Robust quantitative analysis and machine learning in international business research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(7), pages 1307-1314, September.
  96. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Working Papers 23-15, Federal Reserve Bank of Cleveland.
  97. Engel, Christoph, 2020. "Estimating heterogeneous reactions to experimental treatments," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 124-147.
  98. Stephen Jarvis & Olivier Deschenes & Akshaya Jha, 2022. "The Private and External Costs of Germany’s Nuclear Phase-Out," Journal of the European Economic Association, European Economic Association, vol. 20(3), pages 1311-1346.
  99. Lai Xinglin, 2021. "Modelling hetegeneous treatment effects by quantitle local polynomial decision tree and forest," Papers 2111.15320, arXiv.org, revised Mar 2022.
  100. Youmi Suk, 2024. "A Within-Group Approach to Ensemble Machine Learning Methods for Causal Inference in Multilevel Studies," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 61-91, February.
  101. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
  102. Maldonado, Sebastián & Domínguez, Gonzalo & Olaya, Diego & Verbeke, Wouter, 2021. "Profit-driven churn prediction for the mutual fund industry: A multisegment approach," Omega, Elsevier, vol. 100(C).
  103. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  104. Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
  105. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers 61/17, Institute for Fiscal Studies.
  106. Eliaz, Kfir & Spiegler, Ran, 2022. "On incentive-compatible estimators," Games and Economic Behavior, Elsevier, vol. 132(C), pages 204-220.
  107. Pamela Giustinelli & Matthew D. Shapiro, 2024. "SeaTE: Subjective Ex Ante Treatment Effect of Health on Retirement," American Economic Journal: Applied Economics, American Economic Association, vol. 16(2), pages 278-317, April.
  108. Taiyo Fukai & Hidehiko Ichimura & Keisuke Kawata, 2021. "Describing the impacts of COVID-19 on the labor market in Japan until June 2020," The Japanese Economic Review, Springer, vol. 72(3), pages 439-470, July.
  109. Salomo Hirvonen & Maarit Lassander & Lauri Sääksvuori & Janne Tukiainen, 2023. "Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young voters," Discussion Papers 157, Aboa Centre for Economics.
  110. Daisuke Moriwaki & Soichiro Harada & Jiyan Schneider & Takahiro Hoshino, 2020. "Nudging Preventive Behaviors in COVID-19 Crisis: A Large Scale RCT using Smartphone Advertising," Keio-IES Discussion Paper Series 2020-021, Institute for Economics Studies, Keio University.
  111. Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
  112. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," SciencePo Working papers Main hal-03455978, HAL.
  113. Elena Denisova-Schmidt & Martin Huber & Elvira Leontyeva & Anna Solovyeva, 2021. "Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students," Empirical Economics, Springer, vol. 60(4), pages 1661-1684, April.
  114. Samuel Bazzi & Lisa Cameron & Simone Schaner & Firman Witoelar, 2021. "Information, Intermediaries, and International Migration," Melbourne Institute Working Paper Series wp2021n30, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  115. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
  116. Mesplé-Somps, Sandrine & Nilsson, Björn, 2023. "Role models, aspirations and desire to migrate," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 819-839.
  117. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
  118. Layla Parast & Tianxi Cai & Lu Tian, 2023. "Testing for heterogeneity in the utility of a surrogate marker," Biometrics, The International Biometric Society, vol. 79(2), pages 799-810, June.
  119. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
  120. Sandra Jaworeck, 2022. "A New Approach for Constructing a Health Care Index including the Subjective Level," IJERPH, MDPI, vol. 19(15), pages 1-16, August.
  121. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
  122. Timothée Demont & Daniela Horta Sáenz & Eva Raiber, 2023. "Turning worries into cognitive performance: Results from an online experiment during Covid," AMSE Working Papers 2302, Aix-Marseille School of Economics, France.
  123. Saskia Opitz & Dirk Sliwka & Timo Vogelsang & Tom Zimmermann, 2022. "The Targeted Assignment of Incentive Schemes," ECONtribute Discussion Papers Series 187, University of Bonn and University of Cologne, Germany.
  124. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
  125. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
  126. Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
  127. Yike Wang & Chris Gu & Taisuke Otsu, 2024. "Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity," Papers 2401.16275, arXiv.org.
  128. Kwonsang Lee & Dylan S. Small & Paul R. Rosenbaum, 2018. "A powerful approach to the study of moderate effect modification in observational studies," Biometrics, The International Biometric Society, vol. 74(4), pages 1161-1170, December.
  129. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
  130. Berry, James & Kim, Hyuncheol Bryant & Son, Hyuk Harry, 2022. "When student incentives do not work: Evidence from a field experiment in Malawi," Journal of Development Economics, Elsevier, vol. 158(C).
  131. Jason Abaluck & Leila Agha & David C. Chan Jr & Daniel Singer & Diana Zhu, 2020. "Fixing Misallocation with Guidelines: Awareness vs. Adherence," NBER Working Papers 27467, National Bureau of Economic Research, Inc.
  132. Sandra García- Uribe, 2018. "The effects of tax changes on economic activity: a narrative approach to frequent anticipations," Working Papers 1828, Banco de España.
  133. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
  134. Hayakawa, Kazunobu & Keola, Souknilanh & Silaphet, Korrakoun & Yamanouchi, Kenta, 2022. "Estimating the impacts of international bridges on foreign firm locations: a machine learning approach," IDE Discussion Papers 847, Institute of Developing Economies, Japan External Trade Organization(JETRO).
  135. Xueming Luo & Xianghua Lu & Jing Li, 2019. "When and How to Leverage E-commerce Cart Targeting: The Relative and Moderated Effects of Scarcity and Price Incentives with a Two-Stage Field Experiment and Causal Forest Optimization," Information Systems Research, INFORMS, vol. 30(4), pages 1203-1227, December.
  136. Letta,Marco & Montalbano,Pierluigi & Paolantonio,Adriana, 2024. "Climate Immobility Traps : A Household-Level Test," Policy Research Working Paper Series 10724, The World Bank.
  137. Chang Cai & Sandy Dall’Erba, 2021. "On the evaluation of heterogeneous climate change impacts on US agriculture: does group membership matter?," Climatic Change, Springer, vol. 167(1), pages 1-23, July.
  138. Christopher Adjaho & Timothy Christensen, 2022. "Externally Valid Policy Choice," Papers 2205.05561, arXiv.org, revised Jul 2023.
  139. Vinish Shrestha, 2024. "Heterogeneous Impacts of ACA-Medicaid Expansion on Insurance and Labor Market Outcomes in the American South," Working Papers 2024-08, Towson University, Department of Economics, revised Jun 2024.
  140. Patrick Rehill, 2024. "How do applied researchers use the Causal Forest? A methodological review of a method," Papers 2404.13356, arXiv.org.
  141. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
  142. Sarah Friedrich & Gerd Antes & Sigrid Behr & Harald Binder & Werner Brannath & Florian Dumpert & Katja Ickstadt & Hans A. Kestler & Johannes Lederer & Heinz Leitgöb & Markus Pauly & Ansgar Steland & A, 2022. "Is there a role for statistics in artificial intelligence?," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 823-846, December.
  143. Netta Barak‐Corren & Yoav Kan‐Tor & Nelson Tebbe, 2022. "Examining the effects of antidiscrimination laws on children in the foster care and adoption systems," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(4), pages 1003-1066, December.
  144. Cevat Giray Aksoy & Christopher S. Carpenter & Ralph De Haas & Mathias Dolls & Lisa Windsteiger, 2023. "Reducing Sexual Orientation Discrimination: Experimental Evidence from Basic Information Treatments," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(1), pages 35-59, January.
  145. Elgin, Dallas J., 2018. "Utilizing predictive modeling to enhance policy and practice through improved identification of at-risk clients: Predicting permanency for foster children," Children and Youth Services Review, Elsevier, vol. 91(C), pages 156-167.
  146. Athey, Susan & Imbens, Guido W. & Metzger, Jonas & Munro, Evan, 2024. "Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 240(2).
  147. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
  148. Ajzenman, Nicolas & Luna, Laura Becerra & Hernández-Agramonte, Juan Manuel & Lopez Boo, Florencia & Perez Alfaro, Marcelo & Vásquez-Echeverría, Alejandro & Mateo Diaz, Mercedes, 2022. "A behavioral intervention to increase preschool attendance in Uruguay," Journal of Development Economics, Elsevier, vol. 159(C).
  149. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
  150. Daniel Goller & Michael C. Knaus & Michael Lechner & Gabriel Okasa, 2021. "Predicting match outcomes in football by an Ordered Forest estimator," Chapters, in: Ruud H. Koning & Stefan Kesenne (ed.), A Modern Guide to Sports Economics, chapter 22, pages 335-355, Edward Elgar Publishing.
  151. Paul B. Ellickson & Wreetabrata Kar & James C. Reeder, 2023. "Estimating Marketing Component Effects: Double Machine Learning from Targeted Digital Promotions," Marketing Science, INFORMS, vol. 42(4), pages 704-728, July.
  152. Oparina, Ekaterina & Krekel, Christian & Srisuma, Sorawoot, 2024. "Talking Therapy: Impacts of a Nationwide Mental Health Service in England," IZA Discussion Papers 16839, Institute of Labor Economics (IZA).
  153. Yusuke Narita & Shota Yasui & Kohei Yata, 2018. "Efficient Counterfactual Learning from Bandit Feedback," Cowles Foundation Discussion Papers 2155, Cowles Foundation for Research in Economics, Yale University.
  154. Yiqi Liu & Yuan Qi, 2023. "Using Forests in Multivariate Regression Discontinuity Designs," Papers 2303.11721, arXiv.org, revised Jul 2024.
  155. Merlin Stein, 2022. "When are large female-led firms more resilient against shocks? Learnings from Indian enterprises during COVID-19 with diff-in-diff and causal forests," CSAE Working Paper Series 2022-01, Centre for the Study of African Economies, University of Oxford.
  156. Xiangyang Bi & Xueling Liu, 2024. "From “transitions” to “trajectories”: towards a holistic interactionistic analysis of educational inequality in contemporary China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-16, December.
  157. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
  158. Nathan Kallus & Miruna Oprescu, 2022. "Robust and Agnostic Learning of Conditional Distributional Treatment Effects," Papers 2205.11486, arXiv.org, revised Feb 2023.
  159. Tim Coleman & Lucas Mentch & Daniel Fink & Frank A. La Sorte & David W. Winkler & Giles Hooker & Wesley M. Hochachka, 2020. "Statistical inference on tree swallow migrations with random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 973-989, August.
  160. AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
  161. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
  162. Nathan Kallus, 2022. "What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment," Papers 2205.10327, arXiv.org, revised Nov 2022.
  163. Maria Nareklishvili & Nicholas Polson & Vadim Sokolov, 2022. "Feature Selection for Personalized Policy Analysis," Papers 2301.00251, arXiv.org, revised Jul 2023.
  164. Chen, Zhongyuan & Xie, Jun, 2023. "Estimating heterogeneous treatment effects versus building individualized treatment rules: Connection and disconnection," Statistics & Probability Letters, Elsevier, vol. 199(C).
  165. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org, revised Oct 2024.
  166. Hua Chen & Jianing Xing & Xiaoxu Yang & Kai Zhan, 2021. "Heterogeneous Effects of Health Insurance on Rural Children’s Health in China: A Causal Machine Learning Approach," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
  167. Qihang Xue & Huimin Wang & Caiquan Bai, 2023. "Local green finance policies and corporate ESG performance," International Review of Finance, International Review of Finance Ltd., vol. 23(4), pages 721-749, December.
  168. Shi, Chengchun & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2021. "An online sequential test for qualitative treatment effects," LSE Research Online Documents on Economics 112521, London School of Economics and Political Science, LSE Library.
  169. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
  170. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  171. Kroczek, Martin & Kugler, Philipp, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," VfS Annual Conference 2022 (Basel): Big Data in Economics 264108, Verein für Socialpolitik / German Economic Association.
  172. Markus Eyting, 2020. "A Random Forest a Day Keeps the Doctor Away," Working Papers 2026, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
  173. Oparina, Ekaterina & Kaiser, Caspar & Gentile, Niccoló & Tkatchenko, Alexandre & Clark, Andrew E. & De Neve, Jan-Emmanuel & D'Ambrosio, Conchita, 2022. "Human wellbeing and machine learning," LSE Research Online Documents on Economics 117955, London School of Economics and Political Science, LSE Library.
  174. Alexander J. Ohnmacht & Arndt Stahler & Sebastian Stintzing & Dominik P. Modest & Julian W. Holch & C. Benedikt Westphalen & Linus Hölzel & Marisa K. Schübel & Ana Galhoz & Ali Farnoud & Minhaz Ud-Dea, 2023. "The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  175. Melvyn Weeks & Tobias Gabel Christiansen, 2020. "Understanding the Distributional Aspects of Microcredit Expansions," Papers 2011.10509, arXiv.org.
  176. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
  177. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
  178. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
  179. Zhaonan Qu & Ruoxuan Xiong & Jizhou Liu & Guido Imbens, 2021. "Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference," Papers 2107.12420, arXiv.org, revised Jun 2024.
  180. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.
  181. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
  182. Nathan Kallus & Xiaojie Mao, 2023. "Stochastic Optimization Forests," Management Science, INFORMS, vol. 69(4), pages 1975-1994, April.
  183. Labro, Eva & Lang, Mark & Omartian, James D., 2023. "Predictive analytics and centralization of authority," Journal of Accounting and Economics, Elsevier, vol. 75(1).
  184. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
  185. Alicia Curth & Mihaela van der Schaar, 2023. "In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation," Papers 2302.02923, arXiv.org, revised Jun 2023.
  186. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
  187. David Rey-Blanco & Pelayo Arbués & Fernando A. López & Antonio Páez, 2024. "Using machine learning to identify spatial market segments. A reproducible study of major Spanish markets," Environment and Planning B, , vol. 51(1), pages 89-108, January.
  188. Radermacher, Jan W., 2023. "Mamma Mia! Revealing hidden heterogeneity by PCA-biplot: MPC puzzle for Italy's elderly poor," SAFE Working Paper Series 382, Leibniz Institute for Financial Research SAFE.
  189. Raymond Duch & Paulina Granados & Denise Laroze & Mauricio Lopez & Marian Ormeño & Ximena Quintanilla, 2021. "La Arquitectura De Elección Mejora La Selección De Pensiones," Working Papers 66, Superintendencia de Pensiones, revised Jan 2021.
  190. Marta Serra-Garcia & Nora Szech, 2023. "Incentives and Defaults Can Increase COVID-19 Vaccine Intentions and Test Demand," Management Science, INFORMS, vol. 69(2), pages 1037-1049, February.
  191. Chen, Jian & Katchova, Ani L. & Zhou, Chenxi, 2021. "Agricultural loan delinquency prediction using machine learning methods," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(5), May.
  192. J. Michelle Brock & Ralph De Haas, 2023. "Discriminatory Lending: Evidence from Bankers in the Lab," American Economic Journal: Applied Economics, American Economic Association, vol. 15(2), pages 31-68, April.
  193. Prothit Sen & Phanish Puranam, 2022. "Do Alliance portfolios encourage or impede new business practice adoption? Theory and evidence from the private equity industry," Strategic Management Journal, Wiley Blackwell, vol. 43(11), pages 2279-2312, November.
  194. repec:ags:aaea22:335586 is not listed on IDEAS
  195. Burgess, Simon & Metcalfe, Robert & Sadoff, Sally, 2021. "Understanding the response to financial and non-financial incentives in education: Field experimental evidence using high-stakes assessments," Economics of Education Review, Elsevier, vol. 85(C).
  196. Shi, Zhentao & Huang, Jingyi, 2023. "Forward-selected panel data approach for program evaluation," Journal of Econometrics, Elsevier, vol. 234(2), pages 512-535.
  197. Nicolaj S{o}ndergaard Muhlbach & Mikkel Slot Nielsen, 2019. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," Papers 1909.03968, arXiv.org, revised Feb 2021.
  198. Gabriel Okasa & Kenneth A. Younge, 2022. "Sample Fit Reliability," Papers 2209.06631, arXiv.org.
  199. Walter W. Zhang & Sanjog Misra, 2022. "Coarse Personalization," Papers 2204.05793, arXiv.org, revised Aug 2024.
  200. Jeffrey Smith, 2022. "Treatment Effect Heterogeneity," Evaluation Review, , vol. 46(5), pages 652-677, October.
  201. Alena Skolkova, 2023. "Instrumental Variable Estimation with Many Instruments Using Elastic-Net IV," CERGE-EI Working Papers wp759, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  202. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
  203. Quinn, Barry, 2022. "Teaching Open Science Analytics in the Age of Financial Technology," QBS Working Paper Series 2022/01, Queen's University Belfast, Queen's Business School.
  204. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
  205. Stephan Martin, 2022. "Estimation of Conditional Random Coefficient Models using Machine Learning Techniques," Papers 2201.08366, arXiv.org.
  206. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
  207. Eva Raiber & Daniela Horta Saenz & Timothée Demont, 2023. "Turning worries into performance: Results from an online experiment during COVID," French Stata Users' Group Meetings 2023 08, Stata Users Group.
  208. Momin M. Malik, 2020. "A Hierarchy of Limitations in Machine Learning," Papers 2002.05193, arXiv.org, revised Feb 2020.
  209. Lihua Lei & Emmanuel J. Candès, 2021. "Conformal inference of counterfactuals and individual treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 911-938, November.
  210. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  211. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
  212. Kawata, Keisuke & Nakabayashi, Masaki, 2023. "Persistent mind: The effects of information provision on policy preferences," Journal of Policy Modeling, Elsevier, vol. 45(3), pages 522-537.
  213. Aksoy, Cevat Giray & Dolls, Mathias & Durante, Ruben & Windsteiger, Lisa, 2021. "Calamities, Common Interests, Shared Identity: What Shapes Social Cohesion in Europe?," CEPR Discussion Papers 16186, C.E.P.R. Discussion Papers.
  214. Martin Cousineau & Vedat Verter & Susan A. Murphy & Joelle Pineau, 2022. "Estimating causal effects with optimization-based methods: A review and empirical comparison," Papers 2203.00097, arXiv.org.
  215. Brian Krauth, 2020. "Peers as treatments," Discussion Papers dp20-08, Department of Economics, Simon Fraser University.
  216. Olga Takács & János Vincze, 2020. "The gender-dependent structure of wages in Hungary: results using machine learning techniques," CERS-IE WORKING PAPERS 2044, Institute of Economics, Centre for Economic and Regional Studies.
  217. Nicolás Urdaneta Andrade, 2021. "¿Hombres "cracks" y mujeres "amables"? Sesgos de género en encuestas de profesores," Documentos CEDE 19557, Universidad de los Andes, Facultad de Economía, CEDE.
  218. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
  219. Aparajithan Venkateswaran & Anirudh Sankar & Arun G. Chandrasekhar & Tyler H. McCormick, 2024. "Robustly estimating heterogeneity in factorial data using Rashomon Partitions," Papers 2404.02141, arXiv.org, revised Aug 2024.
  220. Isabel Hovdahl, 2019. "On the use of machine learning for causal inference in climate economics," Working Papers No 05/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  221. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
  222. Carlos Fern'andez-Lor'ia & Foster Provost & Jesse Anderton & Benjamin Carterette & Praveen Chandar, 2020. "A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation," Papers 2004.11532, arXiv.org, revised Apr 2022.
  223. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
  224. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
  225. Paul Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Sep 2024.
  226. Koryu Sato & Haruko Noguchi & Kosuke Inoue, 2023. "Heterogeneous Treatment Effect of Retirement on Cognitive Function," Working Papers 2306, Waseda University, Faculty of Political Science and Economics.
  227. Hui Lan & Vasilis Syrgkanis, 2023. "Causal Q-Aggregation for CATE Model Selection," Papers 2310.16945, arXiv.org, revised Nov 2023.
  228. Raval, Devesh & Rosenbaum, Ted & Wilson, Nathan E., 2021. "How do machine learning algorithms perform in predicting hospital choices? evidence from changing environments," Journal of Health Economics, Elsevier, vol. 78(C).
  229. Zhanyu Liu & Zishu Ma & Yuqiong Lei, 2023. "Prospects of Mortality Salience for Promoting Sustainable Public Sector Management: A Survey Experiment on Public Service Motivation," Sustainability, MDPI, vol. 15(13), pages 1-18, July.
  230. William Arbour, 2021. "Can recidivism be prevented from behind bars? Evidence from a behavioral program," Working Papers 2021.07, International Network for Economic Research - INFER.
  231. Wei Chen & Karen Xie & Jianwei Liu & Yong Liu, 2019. "How Incumbents Beat Disruptors? Evidence from Hotels’ Responses to Home-sharing Rivals," Working Papers 19-11, NET Institute.
  232. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
  233. Felix Elwert & Tamás Keller & Andreas Kotsadam, 2020. "Rearranging the Desk Chairs: A large randomized field experiment on the effects of close contact on interethnic relations," CERS-IE WORKING PAPERS 2054, Institute of Economics, Centre for Economic and Regional Studies.
  234. Ziwei Cong & Jia Liu & Puneet Manchanda, 2021. "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers 2107.01629, arXiv.org, revised Sep 2022.
  235. Piasenti, Stefano & Valente, Marica & Van Veldhuizen, Roel & Pfeifer, Gregor, 2023. "Does Unfairness Hurt Women? The Effects of Losing Unfair Competitions," Working Papers 2023:7, Lund University, Department of Economics.
  236. Rafael Quintana, 2023. "Embracing complexity in social science research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 15-38, February.
  237. Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.
  238. Olga Takács & János Vincze, 2023. "Heterogeneous wage structure effects: a partial European East-West comparison," CERS-IE WORKING PAPERS 2305, Institute of Economics, Centre for Economic and Regional Studies.
  239. Lunyu Xie & Tianhua Zou & Joshua Linn & Haosheng Yan, 2024. "Can Building Subway Systems Improve Air Quality? New Evidence from Multiple Cities and Machine Learning," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(4), pages 1009-1044, April.
  240. Affeldt, Pauline & Duso, Tomaso & Szücs, Florian, 2021. "25 years of European merger control," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 76, pages 1-62.
  241. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
  242. Asresu Yitayew & Awudu Abdulai & Yigezu A Yigezu, 2022. "Improved agricultural input delivery systems for enhancing technology adoption: evidence from a field experiment in Ethiopia," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(3), pages 527-556.
  243. Noemi Kreif & Andrew Mirelman & Rodrigo Moreno-Serra & Taufik Hidayat, & Karla DiazOrdaz & Marc Suhrcke, 2020. "Who benefits from health insurance? Uncovering heterogeneous policy impacts using causal machine learning," Working Papers 173cherp, Centre for Health Economics, University of York.
  244. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
  245. Kayo Murakami & Hideki Shimada & Yoshiaki Ushifusa & Takanori Ida, 2022. "Heterogeneous Treatment Effects Of Nudge And Rebate: Causal Machine Learning In A Field Experiment On Electricity Conservation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1779-1803, November.
  246. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
  247. repec:ags:aaea22:336009 is not listed on IDEAS
  248. Rahul Singh, 2020. "Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments," Papers 2012.10315, arXiv.org, revised Mar 2023.
  249. Maur,Jean-Christophe & Nedeljkovic,Milan & Von Uexkull,Jan Erik, 2022. "FDI and Trade Outcomes at the Industry Level—A Data-Driven Approach," Policy Research Working Paper Series 9901, The World Bank.
  250. William C. Horrace & Hyunseok Jung & Shane Sanders, 2022. "Network Competition and Team Chemistry in the NBA," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 35-49, January.
  251. Dylong, Patrick & Übelmesser, Silke, 2024. "Intergroup Contact and Exposure to Information about Immigrants: Experimental Evidence," VfS Annual Conference 2024 (Berlin): Upcoming Labor Market Challenges 302334, Verein für Socialpolitik / German Economic Association.
  252. Bernard Koch & Tim Sainburg & Pablo Geraldo & Song Jiang & Yizhou Sun & Jacob Gates Foster, 2021. "A Primer on Deep Learning for Causal Inference," Papers 2110.04442, arXiv.org, revised Nov 2023.
  253. Brandon Cunningham & Jacob LaRiviere & Casey J. Wichman, 2021. "Clustered into control: Heterogeneous causal impacts of water infrastructure failure," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 1417-1439, July.
  254. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
  255. Alpino, Matteo & Hauge, Karen Evelyn & Kotsadam, Andreas & Markussen, Simen, 2022. "Effects of dialogue meetings on sickness absence—Evidence from a large field experiment," Journal of Health Economics, Elsevier, vol. 83(C).
  256. Ravi Kumar & Shahin Boluki & Karl Isler & Jonas Rauch & Darius Walczak, 2022. "Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing," Papers 2205.01875, arXiv.org, revised Dec 2022.
  257. Xuanai Huang & Yaozhong Wang & Ying Chen & Zunguo Hu, 2024. "Green Technology Innovation and Enterprise Performance: An Analysis Based on Causal Machine Learning Models," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
  258. Milan Miric & Lars Bo Jeppesen, 2020. "Does piracy lead to product abandonment or stimulate new product development?: Evidence from mobile platform‐based developer firms," Strategic Management Journal, Wiley Blackwell, vol. 41(12), pages 2155-2184, December.
  259. Dorothee Weiffen & Ghassan Baliki & Tilman Brück, 2022. "Violent conflict moderates food security impacts of agricultural asset transfers in Syria: A heterogeneity analysis using machine learning," HiCN Working Papers 381, Households in Conflict Network.
  260. Joey Blumberg & Gary Thompson, 2022. "Nonparametric segmentation methods: Applications of unsupervised machine learning and revealed preference," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 976-998, May.
  261. Giorgia Barboni & Juan Camilo Cárdenas & Nicolás de Roux, 2022. "Behavioral Messages and Debt Repayment," Documentos CEDE 20257, Universidad de los Andes, Facultad de Economía, CEDE.
  262. Salomo Hirvonen & Jerome Schafer & Janne Tukiainen, 2022. "Policy Feedback and Civic Engagement: Evidence from the Finnish Basic Income Experiment," Discussion Papers 155, Aboa Centre for Economics.
  263. Hintermann, Beat & Schoeman, Beaumont & Molloy, Joseph & Schatzmann, Thomas & Tchervenkov, Christopher & Axhausen, Kay W., 2023. "The impact of COVID-19 on mobility choices in Switzerland," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
  264. Julius Owusu, 2024. "A Nonparametric Test of Heterogeneous Treatment Effects under Interference," Papers 2410.00733, arXiv.org.
  265. Long He & Sheng Liu & Zuo‐Jun Max Shen, 2022. "Smart urban transport and logistics: A business analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3771-3787, October.
  266. Li, Zhong-fei & Zhou, Qi & Chen, Ming & Liu, Qian, 2021. "The impact of COVID-19 on industry-related characteristics and risk contagion," Finance Research Letters, Elsevier, vol. 39(C).
  267. Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
  268. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2021. "Deep Neural Networks for Estimation and Inference," Econometrica, Econometric Society, vol. 89(1), pages 181-213, January.
  269. Feng, Sanying & Kong, Kaidi & Kong, Yinfei & Li, Gaorong & Wang, Zhaoliang, 2022. "Statistical inference of heterogeneous treatment effect based on single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
  270. Olckers, Matthew, 2021. "On track for retirement?," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 76-88.
  271. Ankinée KIRAKOZIAN & Raphaël CHIAPPINI & Nabila ARFAOUI, 2023. "Nudging employees for greener mobility A field experiment," Bordeaux Economics Working Papers 2023-09, Bordeaux School of Economics (BSE).
  272. Patrick Dylong & Silke Uebelmesser, 2023. "Intergroup Contact and Exposure to Information about Immigrants: Experimental Evidence," CESifo Working Paper Series 10808, CESifo.
  273. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  274. Gal Amedi, 2023. "The Determinants of the Transit Accessibility Premium," Bank of Israel Working Papers 2023.12, Bank of Israel.
  275. Lukas Lanz & Roman Briker & Fabiola H. Gerpott, 2024. "Employees Adhere More to Unethical Instructions from Human Than AI Supervisors: Complementing Experimental Evidence with Machine Learning," Journal of Business Ethics, Springer, vol. 189(3), pages 625-646, January.
  276. Kim, Juram & Hong, Suckwon & Kang, Yubin & Lee, Changyong, 2023. "Domain-specific valuation of university technologies using bibliometrics, Jonckheere–Terpstra tests, and data envelopment analysis," Technovation, Elsevier, vol. 122(C).
  277. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
  278. Aziza Usmanova & Ahmed Aziz & Dilshodjon Rakhmonov & Walid Osamy, 2022. "Utilities of Artificial Intelligence in Poverty Prediction: A Review," Sustainability, MDPI, vol. 14(21), pages 1-39, October.
  279. von Zahn, Moritz & Bauer, Kevin & Mihale-Wilson, Cristina & Jagow, Johanna & Speicher, Max & Hinz, Oliver, 2022. "The smart green nudge: Reducing product returns through enriched digital footprints & causal machine learning," SAFE Working Paper Series 363, Leibniz Institute for Financial Research SAFE, revised 2022.
  280. Dimitris Bertsimas & Alison Borenstein & Luca Mingardi & Omid Nohadani & Agni Orfanoudaki & Bartolomeo Stellato & Holly Wiberg & Pankaj Sarin & Dirk J. Varelmann & Vicente Estrada & Carlos Macaya & Iv, 2021. "Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients," Health Care Management Science, Springer, vol. 24(2), pages 339-355, June.
  281. Günter J. Hitsch & Sanjog Misra & Walter W. Zhang, 2024. "Heterogeneous treatment effects and optimal targeting policy evaluation," Quantitative Marketing and Economics (QME), Springer, vol. 22(2), pages 115-168, June.
  282. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
  283. Waddell, Glen R. & McDonough, Robert, 2022. "Mean Convergence, Combinatorics, and Grade-Point Averages," IZA Discussion Papers 15414, Institute of Labor Economics (IZA).
  284. Joshua B. Gilbert & Zachary Himmelsbach & James Soland & Mridul Joshi & Benjamin W. Domingue, 2024. "Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory," Papers 2405.00161, arXiv.org, revised Aug 2024.
  285. Frondel, Manuel & Kussel, Gerhard & Sommer, Stephan & Vance, Colin, 2019. "Local cost for global benefit: The case of wind turbines," Ruhr Economic Papers 791, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, revised 2019.
  286. Olga Takacs & Janos Vincze, 2019. "The gender pay gap in Hungary: new results with a new methodology," CERS-IE WORKING PAPERS 1924, Institute of Economics, Centre for Economic and Regional Studies.
  287. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
  288. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," LSE Research Online Documents on Economics 121374, London School of Economics and Political Science, LSE Library.
  289. Xinkun Nie & Stefan Wager, 2017. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects," Papers 1712.04912, arXiv.org, revised Aug 2020.
  290. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
  291. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
  292. Raaz Dwivedi & Yan Shuo Tan & Briton Park & Mian Wei & Kevin Horgan & David Madigan & Bin Yu, 2020. "Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 135-178, December.
  293. Kleifgen, Eva & Lang, Julia, 2022. "Should I Train Or Should I Go? Estimating Treatment Effects of Retraining on Regional and Occupational Mobility," VfS Annual Conference 2022 (Basel): Big Data in Economics 264069, Verein für Socialpolitik / German Economic Association.
  294. Hazar Altınbaş & Vincenzo Pacelli & Edgardo Sica, 2022. "An Empirical Assessment of the Contagion Determinants in the Euro Area in a Period of Sovereign Debt Risk," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(2), pages 339-371, July.
  295. Amann, Erwin & Rzepka, Sylvi, 2023. "The effect of goal-setting prompts in a blended learning environment—evidence from a field experiment," Economics of Education Review, Elsevier, vol. 92(C).
  296. Huntington-Klein Nick, 2020. "Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 182-208, January.
  297. Olga Takacs & Janos Vincze, 2018. "The within-job gender pay gap in Hungary," CERS-IE WORKING PAPERS 1834, Institute of Economics, Centre for Economic and Regional Studies.
  298. Dutt, Satyajit & Radermacher, Jan W., 2023. "Age, wealth, and the MPC in Europe: A supervised machine learning approach," SAFE Working Paper Series 383, Leibniz Institute for Financial Research SAFE.
  299. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
  300. Andor, Mark A. & Fels, Katja M. & Renz, Jan & Rzepka, Sylvi, 2018. "Do planning prompts increase educational success? Evidence from randomized controlled trials in MOOCs," Ruhr Economic Papers 790, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  301. Blesse, Sebastian, 2021. "Are your tax problems an opportunity not to pay taxes? Evidence from a randomized survey experiment," ZEW Discussion Papers 21-040, ZEW - Leibniz Centre for European Economic Research.
  302. Jocteur, Bérénice-Alexia & Maume-Deschamps, Véronique & Ribereau, Pierre, 2024. "Heterogeneous Treatment Effect-based Random Forest: HTERF," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
  303. Michelle Acampora & Francesco Capozza & Vahid Moghani, 2022. "Mental Health Literacy, Beliefs and Demand for Mental Health Support among University Students," Tinbergen Institute Discussion Papers 22-079/I, Tinbergen Institute.
  304. Bo Cowgill, 2019. "Bias and Productivity in Humans and Machines," Upjohn Working Papers 19-309, W.E. Upjohn Institute for Employment Research.
  305. Lee, Sungho & Jo, Jingyeong, 2018. "Government R&D Support for SMEs: Policy Effects and Improvement Measures," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 40(4), pages 47-63.
  306. Verhagen, Mark D., 2023. "Using machine learning to monitor the equity of large-scale policy interventions: The Dutch decentralisation of the Social Domain," SocArXiv qzm7y, Center for Open Science.
  307. Di Fang & Michael R. Thomsen & Rodolfo M. Nayga & Aaron M. Novotny, 2019. "WIC Participation and Relative Quality of Household Food Purchases: Evidence from FoodAPS," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 83-105, July.
  308. Cookson, J. Anthony & Gilje, Erik P. & Heimer, Rawley Z., 2022. "Shale shocked: Cash windfalls and household debt repayment," Journal of Financial Economics, Elsevier, vol. 146(3), pages 905-931.
  309. Anna Schwarz & Philipp Warum, 2023. "Don’t Stop Believin’ – Heterogeneous Updating of Intergenerational Mobility Perceptions across Income Groups," CESifo Working Paper Series 10592, CESifo.
  310. Baojiang Chen & Ao Yuan & Jing Qin, 2022. "Pool adjacent violators algorithm–assisted learning with application on estimating optimal individualized treatment regimes," Biometrics, The International Biometric Society, vol. 78(4), pages 1475-1488, December.
  311. Andreas Heusler & Dominik Molitor & Martin Spann, 2019. "How Knowledge Stock Exchanges can increase student success in Massive Open Online Courses," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-17, September.
  312. Haushofer, Johannes & Niehaus, Paul & Paramo, Carlos & Miguel, Edward & Walker, Michael W, 2022. "Targeting Impact Versus Deprivation," Department of Economics, Working Paper Series qt07j8n9vz, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  313. Mengxia Zhang & Lan Luo, 2023. "Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp," Management Science, INFORMS, vol. 69(1), pages 25-50, January.
  314. Linda Hagen & Kosuke Uetake & Nathan Yang & Bryan Bollinger & Allison J. B. Chaney & Daria Dzyabura & Jordan Etkin & Avi Goldfarb & Liu Liu & K. Sudhir & Yanwen Wang & James R. Wright & Ying Zhu, 2020. "How can machine learning aid behavioral marketing research?," Marketing Letters, Springer, vol. 31(4), pages 361-370, December.
  315. Arthur Charpentier & Emmanuel Flachaire & Ewen Gallic, 2023. "Optimal Transport for Counterfactual Estimation: A Method for Causal Inference," Papers 2301.07755, arXiv.org.
  316. Franco Mairuzzo & Peter Ormosi, 2022. "Do the poor pay more for increasing market concentration? A study of retail petroleum," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP) 2021-08, Centre for Competition Policy, University of East Anglia, Norwich, UK..
  317. Tomomi Tanaka, 2019. "Human Capital Development in Ghana," World Bank Publications - Reports 34181, The World Bank Group.
  318. Anna Baiardi & Andrea A Naghi, 2024. "The value added of machine learning to causal inference: evidence from revisited studies," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 213-234.
  319. Fongoni, Marco & Norris, Jonathan & Romiti, Agnese & Shi, Zhan, 2022. "Reference Dependent Aspirations and Peer Effects in Education," IZA Discussion Papers 15785, Institute of Labor Economics (IZA).
  320. Jonathan A. Cook & Saad Siddiqui, 2020. "Random forests and selected samples," Bulletin of Economic Research, Wiley Blackwell, vol. 72(3), pages 272-287, July.
  321. Jaime Ramirez-Cuellar, 2023. "Testing for idiosyncratic Treatment Effect Heterogeneity," Papers 2304.01141, arXiv.org.
  322. Retsef Levi & Elisabeth Paulson & Georgia Perakis & Emily Zhang, 2024. "Heterogeneous Treatment Effects in Panel Data," Papers 2406.05633, arXiv.org.
  323. Niklas M. Witzig, 2024. "Cognitive Noise and Altruistic Preferences," Papers 2410.07647, arXiv.org.
  324. Joe Cooprider & Shima Nassiri, 2023. "Science of price experimentation at Amazon," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 58(1), pages 34-41, January.
  325. Baihua He & Tingyan Zhong & Jian Huang & Yanyan Liu & Qingzhao Zhang & Shuangge Ma, 2021. "Histopathological imaging‐based cancer heterogeneity analysis via penalized fusion with model averaging," Biometrics, The International Biometric Society, vol. 77(4), pages 1397-1408, December.
  326. Youmi Suk & Hyunseung Kang, 2022. "Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 310-343, March.
  327. Bodendorf, Frank & Xie, Qiao & Merkl, Philipp & Franke, Jörg, 2022. "A multi-perspective approach to support collaborative cost management in supplier-buyer dyads," International Journal of Production Economics, Elsevier, vol. 245(C).
  328. Andor, Mark A. & Gerster, Andreas & Peters, Jörg, 2022. "Information campaigns for residential energy conservation," European Economic Review, Elsevier, vol. 144(C).
  329. Michela Carlana & Eliana La Ferrara & Paolo Pinotti, 2022. "Goals and Gaps: Educational Careers of Immigrant Children," Econometrica, Econometric Society, vol. 90(1), pages 1-29, January.
  330. Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
  331. Christian M. Dahl & Torben S. D. Johansen & Emil N. S{o}rensen & Christian E. Westermann & Simon F. Wittrock, 2021. "Applications of Machine Learning in Document Digitisation," Papers 2102.03239, arXiv.org.
  332. Yingjie Zhang & Beibei Li & Xueming Luo & Xiaoyi Wang, 2019. "Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 787-804, September.
  333. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
  334. Julius Owusu, 2023. "Randomization Inference of Heterogeneous Treatment Effects under Network Interference," Papers 2308.00202, arXiv.org, revised Jan 2024.
  335. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
  336. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
  337. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.
  338. Vincent P. Roberdel & Ioulia V. Ossokina & Vladimir A. Karamychev & Theo A. Arentze, 2023. "Energy-efficient homes: effects on poverty, environment and comfort," Tinbergen Institute Discussion Papers 23-082/V, Tinbergen Institute.
  339. Dean Eckles & Maurits Kaptein, 2019. "Bootstrap Thompson Sampling and Sequential Decision Problems in the Behavioral Sciences," SAGE Open, , vol. 9(2), pages 21582440198, June.
  340. Maria Josefsson & Michael J. Daniels, 2021. "Bayesian semi‐parametric G‐computation for causal inference in a cohort study with MNAR dropout and death," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 398-414, March.
  341. Chengfeng Yu & Jiyu Yu & Da Gao, 2024. "Smart Cities and Greener Futures: Evidence from a Quasi-Natural Experiment in China’s Smart City Construction," Sustainability, MDPI, vol. 16(2), pages 1-28, January.
  342. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
  343. Huntington-Klein Nick, 2020. "Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 182-208, January.
  344. Miele, Kai R., 2024. "Mental Health and Labor Market Effects of Anticipating Job Loss," CINCH Working Paper Series (since 2020) 82169, Duisburg-Essen University Library, DuEPublico.
  345. Grimm, Veronika & Kretschmer, Sandra & Mehl, Simon, 2020. "Green innovations: The organizational setup of pilot projects and its influence on consumer perceptions," Energy Policy, Elsevier, vol. 142(C).
  346. Xinyu Li & Wang Miao & Fang Lu & Xiao‐Hua Zhou, 2023. "Improving efficiency of inference in clinical trials with external control data," Biometrics, The International Biometric Society, vol. 79(1), pages 394-403, March.
  347. Cho, Youngjoo & Zhan, Xiang & Ghosh, Debashis, 2022. "Nonlinear predictive directions in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  348. Teck-Hua Ho & Noah Lim & Sadat Reza & Xiaoyu Xia, 2017. "OM Forum—Causal Inference Models in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 509-525, October.
  349. Pengzhou Wu & Kenji Fukumizu, 2021. "Towards Principled Causal Effect Estimation by Deep Identifiable Models," Papers 2109.15062, arXiv.org, revised Nov 2021.
  350. Evans, David K. & Gale, Charles & Kosec, Katrina, 2023. "The educational impacts of cash transfers in Tanzania," Economics of Education Review, Elsevier, vol. 92(C).
  351. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
  352. Weicong Lyu & Jee-Seon Kim & Youmi Suk, 2023. "Estimating Heterogeneous Treatment Effects Within Latent Class Multilevel Models: A Bayesian Approach," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 3-36, February.
  353. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
  354. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
  355. William Arbour & Guy Lacroix & Steeve Marchand, 2021. "Prison Rehabilitation Programs: Efficiency and Targeting," Working Papers tecipa-684, University of Toronto, Department of Economics.
  356. Tim P. Morrison & Art B. Owen, 2022. "Multivariate Tie-breaker Designs," Papers 2202.10030, arXiv.org, revised Oct 2024.
  357. Hyung G. Park & Danni Wu & Eva Petkova & Thaddeus Tarpey & R. Todd Ogden, 2023. "Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 397-418, July.
  358. Zongwu Cai & Pixiong Chen, 2024. "Online Investor Sentiment via Machine Learning," Mathematics, MDPI, vol. 12(20), pages 1-14, October.
  359. M. Kate Bundorf & Maria Polyakova & Ming Tai-Seale, 2019. "How do Humans Interact with Algorithms? Experimental Evidence from Health Insurance," NBER Working Papers 25976, National Bureau of Economic Research, Inc.
  360. Kirk Bansak, 2021. "Estimating causal moderation effects with randomized treatments and non‐randomized moderators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 65-86, January.
  361. Yao Cui & Andrew M. Davis, 2022. "Tax-Induced Inequalities in the Sharing Economy," Management Science, INFORMS, vol. 68(10), pages 7202-7220, October.
  362. Kelvin Mulungu & Zewdu Ayalew Abro & Wambui Beatrice Muriithi & Menale Kassie & Miachael Kidoido & Subramanian Sevgan & Samira Mohamed & Chrysantus Tanga & Fathiya Khamis, 2024. "One size does not fit all: Heterogeneous economic impact of integrated pest management practices for mango fruit flies in Kenya—a machine learning approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 261-279, February.
  363. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
  364. Mao, Xiaojun & Peng, Liuhua & Wang, Zhonglei, 2022. "Nonparametric feature selection by random forests and deep neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
  365. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2022. "Choosing Who Chooses: Selection-Driven Targeting in Energy Rebate Programs," NBER Working Papers 30469, National Bureau of Economic Research, Inc.
  366. Alex Armand & Britta Augsburg & Antonella Bancalari, 2021. "Coordination and the poor maintenance trap: an experiment on public infrastructure in India," IFS Working Papers W21/16, Institute for Fiscal Studies.
  367. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2019. "Individual-level social influence identification in social media: A learning-simulation coordinated method," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1005-1015.
  368. Lechner, Michael & Okasa, Gabriel, 2019. "Random Forest Estimation of the Ordered Choice Model," Economics Working Paper Series 1908, University of St. Gallen, School of Economics and Political Science.
  369. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2022. "Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement," Papers 2201.07055, arXiv.org, revised Oct 2022.
  370. Carlos Fernández-Loría & Foster Provost, 2022. "Causal Decision Making and Causal Effect Estimation Are Not the Same…and Why It Matters," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 4-16, April.
  371. Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
  372. Faltings, Richard & Krumer, Alex & Lechner, Michael, 2019. "Rot-Jaune-Verde. Language and Favoritism: Evidence from Swiss Soccer," Economics Working Paper Series 1915, University of St. Gallen, School of Economics and Political Science.
  373. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
  374. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2021. "Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs," Papers 2112.09850, arXiv.org.
  375. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
  376. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
  377. Milan Miric & Nan Jia & Kenneth G. Huang, 2023. "Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents," Strategic Management Journal, Wiley Blackwell, vol. 44(2), pages 491-519, February.
  378. Breda, Thomas & Grenet, Julien & Monnet, Marion & Van Effenterre, Clémentine, 2020. "Do Female Role Models Reduce the Gender Gap in Science? Evidence from French High Schools," IZA Discussion Papers 13163, Institute of Labor Economics (IZA).
  379. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
  380. Farbmacher, Helmut & Kögel, Heinrich & Spindler, Martin, 2021. "Heterogeneous effects of poverty on attention," Labour Economics, Elsevier, vol. 71(C).
  381. Anna Schwarz & Philipp Warum, 2023. "Don't Stop Believin'. Heterogeneous Updating of Intergenerational Mobility Perceptions across Income Groups," WIFO Working Papers 665, WIFO.
  382. Berkes, Jan & Bouguen, Adrien & Filmer, Deon & Fukao, Tsuyoshi, 2024. "Improving preschool provision and encouraging-demand: Evidence from a large-scale construction program," Journal of Public Economics, Elsevier, vol. 230(C).
  383. Haoyu Wei & Hengrui Cai & Chengchun Shi & Rui Song, 2024. "On Efficient Inference of Causal Effects with Multiple Mediators," Papers 2401.05517, arXiv.org.
  384. Martin Kroczek & Philipp Kugler, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," IAW Discussion Papers 139, Institut für Angewandte Wirtschaftsforschung (IAW).
  385. Patrick Rehill & Nicholas Biddle, 2024. "Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning," Papers 2401.07075, arXiv.org, revised Mar 2024.
  386. Dana Turjeman & Fred M. Feinberg, 2024. "When the Data Are Out: Measuring Behavioral Changes Following a Data Breach," Marketing Science, INFORMS, vol. 43(2), pages 440-461, March.
  387. Charles B. Perkins & J. Christina Wang, 2019. "How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data," Working Papers 19-16, Federal Reserve Bank of Boston.
  388. Ramosaj, Burim & Pauly, Markus, 2019. "Consistent estimation of residual variance with random forest Out-Of-Bag errors," Statistics & Probability Letters, Elsevier, vol. 151(C), pages 49-57.
  389. ARATA Yoshiyuki & MIYAKAWA Daisuke, 2022. "Demand Shock Propagation Through an Input-output Network in Japan," Discussion papers 22027, Research Institute of Economy, Trade and Industry (RIETI).
  390. Yifei Sun & Sy Han Chiou & Mei‐Cheng Wang, 2020. "ROC‐guided survival trees and ensembles," Biometrics, The International Biometric Society, vol. 76(4), pages 1177-1189, December.
  391. Miller, Steve, 2020. "Causal forest estimation of heterogeneous and time-varying environmental policy effects," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
  392. Elisa Stumpf & Silke Uebelmesser, 2024. "Lifting the Veil of Ignorance – Survey Experiments on Preferences for Wealth Redistribution," CESifo Working Paper Series 11126, CESifo.
  393. Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
  394. Ogundari, Kolawole, 2021. "A systematic review of statistical methods for estimating an education production function," MPRA Paper 105283, University Library of Munich, Germany.
  395. Wu, Suofei & Hannig, Jan & Lee, Thomas C.M., 2022. "Uncertainty quantification for honest regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
  396. Abadie, Alberto & Gu, Jiaying & Shen, Shu, 2024. "Instrumental variable estimation with first-stage heterogeneity," Journal of Econometrics, Elsevier, vol. 240(2).
  397. Kerda Varaku & Robin Sickles, 2023. "Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks," Empirical Economics, Springer, vol. 64(6), pages 3121-3165, June.
  398. Achim Ahrens & Alessandra Stampi-Bombelli & Selina Kurer & Dominik Hangartner, 2023. "Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization," Papers 2305.00545, arXiv.org, revised Feb 2024.
  399. Pengzhou Wu & Kenji Fukumizu, 2021. "$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap," Papers 2110.05225, arXiv.org.
  400. Doleac, Jennifer & Eckhouse, Laurel & Foster-Moore, Eric & Harris, Allison & Walker, Hannah & White, Ariel, 2022. "Registering Returning Citizens to Vote," IZA Discussion Papers 15121, Institute of Labor Economics (IZA).
  401. Martina Pocchiari & Jason M.T. Roos, 2023. "The Heterogeneous Effect of Digitizing Community Activities on Community Participation," CESifo Working Paper Series 10841, CESifo.
  402. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
  403. Akbari, Amir & Ng, Lilian & Solnik, Bruno, 2021. "Drivers of economic and financial integration: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 82-102.
  404. Hugo Bodory & Martin Huber & Lukáš Lafférs, 2022. "Evaluating (weighted) dynamic treatment effects by double machine learning [Identification of causal effects using instrumental variables]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 628-648.
  405. Koen Pauwels & Michael Peran & Zee Shah & German Schnaidt & Dauwe Vercamer, 2023. "Sponsored brands video rings up clicks and sales in the short and long run," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 275-286, September.
  406. Daniel O. Scharfstein & Jon Steingrimsson & Aidan McDermott & Chenguang Wang & Souvik Ray & Aimee Campbell & Edward Nunes & Abigail Matthews, 2022. "Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders," Biometrics, The International Biometric Society, vol. 78(2), pages 649-659, June.
  407. Olga Takacs & Janos Vincze, 2019. "Blinder-Oaxaca decomposition with recursive tree-based methods: a technical note," CERS-IE WORKING PAPERS 1923, Institute of Economics, Centre for Economic and Regional Studies.
  408. Singhal, Puja & Hobbs, Andrew, 2021. "The Distribution of Energy Efficiency and Regional Inequality," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242343, Verein für Socialpolitik / German Economic Association.
  409. Zimmert, Franziska & Zimmert, Michael, 2020. "Paid parental leave and maternal reemployment: Do part-time subsidies help or harm?," Economics Working Paper Series 2002, University of St. Gallen, School of Economics and Political Science.
  410. Cerqua, Augusto & Letta, Marco, 2020. "Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories," MPRA Paper 104404, University Library of Munich, Germany.
  411. Arata, Yoshiyuki & Miyakawa, Daisuke, 2024. "Demand shock propagation through input-output linkages in Japan," Journal of Economic Behavior & Organization, Elsevier, vol. 219(C), pages 262-283.
  412. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.
  413. Seojeong Lee & Youngki Shin, 2021. "Complete subset averaging with many instruments," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 290-314.
  414. Ariadna García-Prado & Paula González & Yolanda F. Rebollo-Sanz, 2024. "Confinement policies: controlling contagion without compromising mental health," Working Papers 24.03, Universidad Pablo de Olavide, Department of Economics.
  415. Max Biggs & Rim Hariss & Georgia Perakis, 2023. "Constrained optimization of objective functions determined from random forests," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 397-415, February.
  416. Cai, Hengrui & Shi, Chengchun & Song, Rui & Lu, Wenbin, 2023. "Jump interval-learning for individualized decision making with continuous treatments," LSE Research Online Documents on Economics 118231, London School of Economics and Political Science, LSE Library.
  417. Kevin Li, 2020. "Asymptotic Normality for Multivariate Random Forest Estimators," Papers 2012.03486, arXiv.org, revised Jan 2021.
  418. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
  419. Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
  420. Mika Ylinen & Mikko Ranta, 2024. "Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 247-276, March.
  421. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
  422. Yixin Tang & Yicong Lin & Navdeep S. Sahni, 2023. "Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash," Papers 2311.14698, arXiv.org, revised Nov 2023.
  423. Riccardo Di Francesco, 2023. "Ordered Correlation Forest," Papers 2309.08755, arXiv.org.
  424. Matthew Elliott & Lisa Elliott & Evert Van der Sluis, 2018. "A Predictive Analytics Understanding of Cooperative Membership Heterogeneity and Sustainability," Sustainability, MDPI, vol. 10(6), pages 1-31, June.
  425. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
  426. Yujie Xu & Vivian Loftness & Edson Severnini, 2021. "Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio," Energies, MDPI, vol. 14(14), pages 1-24, July.
  427. Roberto Cerina & Raymond Duch, 2021. "Polling India via regression and post-stratification of non-probability online samples," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-34, November.
  428. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
  429. Verbeke, Wouter & Olaya, Diego & Guerry, Marie-Anne & Van Belle, Jente, 2023. "To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates," European Journal of Operational Research, Elsevier, vol. 305(2), pages 838-852.
  430. Takahiro Hoshino & Keisuke Takahata, 2018. "Identification of heterogeneous treatment effects as a function of potential untreated outcome under the nonignorable assignment condition," Keio-IES Discussion Paper Series 2018-005, Institute for Economics Studies, Keio University.
  431. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
  432. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org.
  433. Bas Bosma & Arjen Witteloostuijn, 2024. "Machine learning in international business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 55(6), pages 676-702, August.
  434. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
  435. Vivek F. Farias & Andrew A. Li & Tianyi Peng, 2021. "Learning Treatment Effects in Panels with General Intervention Patterns," Papers 2106.02780, arXiv.org, revised Mar 2023.
  436. Andres Liberman & Christopher A. Neilson & Luis Opazo & Seth Zimmerman, 2019. "Equilibrium Effects of Asymmetric Information on Consumer Credit Markets," Working Papers 2019-7, Princeton University. Economics Department..
  437. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.
  438. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.
  439. Mr. Andrew J Tiffin, 2019. "Machine Learning and Causality: The Impact of Financial Crises on Growth," IMF Working Papers 2019/228, International Monetary Fund.
  440. Alberto Caron & Gianluca Baio & Ioanna Manolopoulou, 2022. "Estimating individual treatment effects using non‐parametric regression models: A review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1115-1149, July.
  441. Roth, Alexander & Schmidt, Felix, 2023. "Not only a mild winter: German consumers change their behavior to save natural gas," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7(6), pages 1081-1086.
  442. Dongcheng Zhang & Kunpeng Zhang, 2020. "Weighting-Based Treatment Effect Estimation via Distribution Learning," Papers 2012.13805, arXiv.org, revised May 2023.
  443. Hodula, Martin & Melecký, Martin & Pfeifer, Lukáš & Szabo, Milan, 2023. "Cooling the mortgage loan market: The effect of borrower-based limits on new mortgage lending," Journal of International Money and Finance, Elsevier, vol. 132(C).
  444. Eoghan O'Neill & Melvyn Weeks, 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Papers 1810.09179, arXiv.org, revised Oct 2019.
  445. Koch, Bernard & Sainburg, Tim & Geraldo, Pablo & JIANG, SONG & Sun, Yizhou & Foster, Jacob G., 2021. "Deep Learning of Potential Outcomes," SocArXiv aeszf, Center for Open Science.
  446. Yu‐Chin Hsu & Shu Shen, 2021. "Testing monotonicity of conditional treatment effects under regression discontinuity designs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 346-366, April.
  447. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org.
  448. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
  449. Cerqua Augusto & Di Stefano Roberta & Mattera Raffaele, 2024. "The Clustered Dose-Response Function Estimator for continuous treatment with heterogeneous treatment effects," Papers 2409.08773, arXiv.org.
  450. Martin Hodula & Milan Szabo & Lukas Pfeifer & Martin Melecky, 2022. "Cooling the Mortgage Loan Market: The Effect of Recommended Borrower-Based Limits on New Mortgage Lending," Working Papers 2022/3, Czech National Bank.
  451. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
  452. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
  453. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
  454. Fengshi Niu & Harsha Nori & Brian Quistorff & Rich Caruana & Donald Ngwe & Aadharsh Kannan, 2022. "Differentially Private Estimation of Heterogeneous Causal Effects," Papers 2202.11043, arXiv.org.
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