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Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence

Citations

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Cited by:

  1. 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.
  2. Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
  3. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
  4. 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).
  5. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
  6. 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).
  7. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
  8. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
  9. 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.
  10. Engberg, Erik & Görg, Holger & Lodefalk, Magnus & Javed, Farrukh & Längkvist, Martin & Monteiro, Natália & Kyvik Nordås, Hildegunn & Pulito, Giuseppe & Schroeder, Sarah & Tang, Aili, 2023. "AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries," Ratio Working Papers 370, The Ratio Institute.
  11. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03455978, HAL.
  12. 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).
  13. 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.
  14. Xinru WANG & Nina DELIU & NARITA Yusuke & Bibhas CHAKRABORTY, 2023. "SMART-EXAM: Incorporating Participants' Welfare into Sequential Multiple Assignment Randomized Trials," Discussion papers 23081, Research Institute of Economy, Trade and Industry (RIETI).
  15. Jacob, Daniel, 2020. "Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects," IRTG 1792 Discussion Papers 2020-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  16. Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series 2108, University of St. Gallen, School of Economics and Political Science.
  17. Quintana-Domeque, Climent & Zeng, Jingya, 2023. "COVID-19 and Mental Health: Natural Experiments of the Costs of Lockdowns," IZA Discussion Papers 16532, Institute of Labor Economics (IZA).
  18. 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.
  19. 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.
  20. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
  21. 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).
  22. 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.
  23. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
  24. Sushant More & Priya Kotwal & Sujith Chappidi & Dinesh Mandalapu & Chris Khawand, 2024. "Double Machine Learning at Scale to Predict Causal Impact of Customer Actions," Papers 2409.02332, arXiv.org.
  25. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
  26. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
  27. 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.
  28. 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.
  29. Daniele Ballinari & Nora Bearth, 2024. "Improving the Finite Sample Performance of Double/Debiased Machine Learning with Propensity Score Calibration," Papers 2409.04874, arXiv.org.
  30. Haupt, Johannes & Lessmann, Stefan, 2022. "Targeting customers under response-dependent costs," European Journal of Operational Research, Elsevier, vol. 297(1), pages 369-379.
  31. 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.
  32. 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.
  33. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
  34. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
  35. Joshua D. Angrist & Brigham Frandsen, 2022. "Machine Labor," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 97-140.
  36. Haupt, Johannes & Lessmann, Stefan, 2020. "Targeting Cutsomers Under Response-Dependent Costs," IRTG 1792 Discussion Papers 2020-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  37. 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.
  38. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
  39. Haupt, Johannes & Jacob, Daniel & Gubela, Robin M. & Lessmann, Stefan, 2019. "Affordable Uplift: Supervised Randomization in Controlled Exprtiments," IRTG 1792 Discussion Papers 2019-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  40. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
  41. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
  42. Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "The Heterogeneous Effects of Active Labour Market Policies in Switzerland," Papers 2410.23322, arXiv.org.
  43. Roberto Esposti, 2022. "Non-Monetary Motivations Of Agroenvironmental Policies Adoption. A Causal Forest Approach," Working Papers 459, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
  44. Ogundari, Kolawole, 2021. "A systematic review of statistical methods for estimating an education production function," MPRA Paper 105283, University Library of Munich, Germany.
  45. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org.
  46. Johannes Haupt & Stefan Lessmann, 2020. "Targeting customers under response-dependent costs," Papers 2003.06271, arXiv.org, revised Aug 2021.
  47. Burlat, Héloïse, 2024. "Everybody’s got to learn sometime? A causal machine learning evaluation of training programmes for jobseekers in France," Labour Economics, Elsevier, vol. 89(C).
  48. Roberto Esposti, 2022. "The Coevolution of Policy Support and Farmers' Behaviour. An investigation on Italian agriculture over the 2008-2019 period," Working Papers 464, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
  49. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
  50. Patrick Rehill & Nicholas Biddle, 2022. "Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation," Papers 2212.06312, arXiv.org, revised Oct 2023.
  51. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2023. "Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 194-217, January.
  52. Rafael Quintana, 2023. "Embracing complexity in social science research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 15-38, February.
  53. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  54. Olga Takács & János Vincze, 2023. "Where is the pain the most acute? The market segments particularly affected by gender wage discrimination in Hungary," CERS-IE WORKING PAPERS 2304, Institute of Economics, Centre for Economic and Regional Studies.
  55. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
  56. 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).
  57. Finn Lattimore & Daniel M. Steinberg & Anna Zhu, 2023. "The Economic Effect of Gaining a New Qualification Later in Life," Papers 2304.01490, arXiv.org, revised Apr 2023.
  58. 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.
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