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Machine Learning for Estimating Heterogeneous Causal Effects

Citations

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography for Economics:
  1. > Econometrics > Big Data

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Cited by:

  1. Stefan Wager, 2016. "Comments on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 261-263, June.
  2. Han, Xuehui & Wei, Shang-Jin, 2017. "Re-examining the middle-income trap hypothesis (MITH): What to reject and what to revive?," Journal of International Money and Finance, Elsevier, vol. 73(PA), pages 41-61.
  3. Fafchamps, Marcel & Labonne, Julien, 2017. "Using Split Samples to Improve Inference on Causal Effects," Political Analysis, Cambridge University Press, vol. 25(4), pages 465-482, October.
  4. Aaron Chalfin & Benjamin Hansen & Jason Lerner & Lucie Parker, 2019. "Reducing Crime Through Environmental Design: Evidence from a Randomized Experiment of Street Lighting in New York City," NBER Working Papers 25798, National Bureau of Economic Research, Inc.
  5. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
  6. 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".
  7. W. Bentley MacLeod, 2017. "Viewpoint: The human capital approach to inference," Canadian Journal of Economics, Canadian Economics Association, vol. 50(1), pages 5-39, February.
  8. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
  9. 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).
  10. Vishal Gupta & Brian Rongqing Han & Song-Hee Kim & Hyung Paek, 2020. "Maximizing Intervention Effectiveness," Management Science, INFORMS, vol. 66(12), pages 5576-5598, December.
  11. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
  12. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
  13. Bosker, Maarten, 2022. "City origins," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  14. Sonan Memon, 2021. "Machine Learning for Economists: An Introduction," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 60(2), pages 201-211.
  15. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions," Renewable Energy, Elsevier, vol. 167(C), pages 99-115.
  16. Marcel Fafchamps & Julien Labonne, 2016. "Using Split Samples to Improve Inference about Causal Effects," NBER Working Papers 21842, National Bureau of Economic Research, Inc.
  17. Sophie van Huellen & Duo Qin, 2019. "Compulsory Schooling and Returns to Education: A Re-Examination," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
  18. Jinping Hu, 2023. "Customer feature selection from high-dimensional bank direct marketing data for uplift modeling," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 160-171, June.
  19. Seungwoo Chin & Matthew E. Kahn & Hyungsik Roger Moon, 2020. "Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 48(3), pages 886-914, September.
  20. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
  21. Johannes Haupt & Stefan Lessmann, 2020. "Targeting customers under response-dependent costs," Papers 2003.06271, arXiv.org, revised Aug 2021.
  22. Droste, Nils & Becker, Claudia & Ring, Irene & Santos, Rui, 2017. "Decentralization effects in ecological fiscal transfers: The case of Portugal," UFZ Discussion Papers 3/2017, Helmholtz Centre for Environmental Research (UFZ), Division of Social Sciences (ÖKUS).
  23. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
  24. Jonathan M.V. Davis & Sara B. Heller, 2017. "Rethinking the Benefits of Youth Employment Programs: The Heterogeneous Effects of Summer Jobs," NBER Working Papers 23443, National Bureau of Economic Research, Inc.
  25. Majid Bazarbash, 2019. "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers 2019/109, International Monetary Fund.
  26. Hunt Allcott & Judd B. Kessler, 2015. "The Welfare Effects of Nudges: A Case Study of Energy Use Social Comparisons," NBER Working Papers 21671, National Bureau of Economic Research, Inc.
  27. 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.
  28. Maria Nareklishvili & Nicholas Polson & Vadim Sokolov, 2022. "Feature Selection for Personalized Policy Analysis," Papers 2301.00251, arXiv.org, revised Jul 2023.
  29. Grépin, Karen A. & Habyarimana, James & Jack, William, 2019. "Cash on delivery: Results of a randomized experiment to promote maternal health care in Kenya," Journal of Health Economics, Elsevier, vol. 65(C), pages 15-30.
  30. Enoch Hyunwook Kang & P. R. Kumar, 2023. "Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle," Papers 2301.12571, arXiv.org, revised Jun 2023.
  31. Andr'es Ram'irez-Hassan & Raquel Vargas-Correa & Gustavo Garc'ia & Daniel Londo~no, 2020. "Optimal selection of the number of control units in kNN algorithm to estimate average treatment effects," Papers 2008.06564, arXiv.org.
  32. Daniel Ershov, 2018. "Competing with Superstars in the Mobile App Market," Working Papers 18-02, NET Institute.
  33. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  34. 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.
  35. Chen, Jingnan (Cecilia) & Fonseca, Miguel A. & Grimshaw, Shaun B., 2021. "When a nudge is (not) enough: Experiments on social information and incentives," European Economic Review, Elsevier, vol. 134(C).
  36. Muller, Seán M., 2020. "The implications of a fundamental contradiction in advocating randomized trials for policy," World Development, Elsevier, vol. 127(C).
  37. Florian Gunsilius & Meng Hsuan Hsieh & Myung Jin Lee, 2022. "Tangential Wasserstein Projections," Papers 2207.14727, arXiv.org, revised Aug 2022.
  38. Peysakhovich, Alexander & Naecker, Jeffrey, 2017. "Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 373-384.
  39. Prithwiraj Choudhury & Ryan T. Allen & Michael G. Endres, 2021. "Machine learning for pattern discovery in management research," Strategic Management Journal, Wiley Blackwell, vol. 42(1), pages 30-57, January.
  40. Rafael P. Greminger, 2022. "Heterogeneous Position Effects and the Power of Rankings," Papers 2210.16408, arXiv.org, revised Dec 2023.
  41. Libo Sun & Guodong Lyu & Yugang Yu & Chung‐Piaw Teo, 2020. "Fulfillment by Amazon versus fulfillment by seller: An interpretable risk‐adjusted fulfillment model," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 627-645, December.
  42. Haifeng Li & Mo Hai & Wenxun Tang, 2022. "Prior Knowledge-Based Causal Inference Algorithms and Their Applications for China COVID-19 Analysis," Mathematics, MDPI, vol. 10(19), pages 1-20, September.
  43. Sam Asher & Denis Nekipelov & Paul Novosad & Stephen P. Ryan, 2016. "Classification Trees for Heterogeneous Moment-Based Models," NBER Working Papers 22976, National Bureau of Economic Research, Inc.
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