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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

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Cited by:

  1. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Manuel Hermosilla, 2021. "Rushed Innovation: Evidence from Drug Licensing," Management Science, INFORMS, vol. 67(1), pages 257-278, January.
  13. 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.
  14. Federico Zincenko, 2023. "Nonparametric estimation of conditional densities by generalized random forests," Papers 2309.13251, arXiv.org, revised Jan 2024.
  15. 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.
  16. 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.
  17. 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.
  18. Jeremy Bertomeu, 2020. "Machine learning improves accounting: discussion, implementation and research opportunities," Review of Accounting Studies, Springer, vol. 25(3), pages 1135-1155, September.
  19. 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.
  20. 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).
  21. 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.
  22. Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
  23. 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.
  24. 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.
  25. 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.
  26. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  27. 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.
  28. 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.
  29. Eliaz, Kfir & Spiegler, Ran, 2022. "On incentive-compatible estimators," Games and Economic Behavior, Elsevier, vol. 132(C), pages 204-220.
  30. 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.
  31. 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.
  32. 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).
  33. 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.
  34. 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.
  35. 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.
  36. 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).
  37. 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.
  38. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
  39. 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).
  40. 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).
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Nonbinary, Ordered Treatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org.
  49. 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.
  50. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
  51. Zhaonan Qu & Ruoxuan Xiong & Jizhou Liu & Guido Imbens, 2021. "Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference," Papers 2107.12420, arXiv.org, revised Jun 2022.
  52. 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.
  53. Labro, Eva & Lang, Mark & Omartian, James D., 2023. "Predictive analytics and centralization of authority," Journal of Accounting and Economics, Elsevier, vol. 75(1).
  54. Henning Hermes & Philipp Lergetporer & Fabian Mierisch & Guido Schwerdt & Simon Wiederhold, 2024. "Does Information about Inequality and Discrimination in Early Child Care Affect Policy Preferences?," CESifo Working Paper Series 10925, CESifo.
  55. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
  56. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
  57. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
  58. 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).
  59. 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.
  60. 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.
  61. 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.
  62. Paul Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Dec 2023.
  63. 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.
  64. 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.
  65. 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.
  66. 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.
  67. 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.
  68. 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.
  69. 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.
  70. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
  71. Paudel, Ujjwol, 2023. "Cross-Platforms Merger Effects," 2023 Annual Meeting, July 23-25, Washington D.C. 336009, Agricultural and Applied Economics Association.
  72. 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.
  73. 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.
  74. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03455978, HAL.
  75. 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).
  76. 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.
  77. 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).
  78. Olckers, Matthew, 2021. "On track for retirement?," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 76-88.
  79. 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).
  80. Patrick Dylong & Silke Uebelmesser, 2023. "Intergroup Contact and Exposure to Information about Immigrants: Experimental Evidence," CESifo Working Paper Series 10808, CESifo.
  81. Gal Amedi, 2023. "The Determinants of the Transit Accessibility Premium," Bank of Israel Working Papers 2023.12, Bank of Israel.
  82. 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.
  83. 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.
  84. Waddell, Glen R. & McDonough, Robert, 2022. "Mean Convergence, Combinatorics, and Grade-Point Averages," IZA Discussion Papers 15414, Institute of Labor Economics (IZA).
  85. 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.
  86. Xinkun Nie & Stefan Wager, 2017. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects," Papers 1712.04912, arXiv.org, revised Aug 2020.
  87. 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.
  88. 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.
  89. 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.
  90. 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.
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  92. 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.
  93. 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.
  94. 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.
  95. 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..
  96. Ekaterina Oparina & Caspar Kaiser & Niccolo Gentile & Alexandre Tkatchenko & Andrew E. Clark & Jan-Emmanuel De Neve & Conchita D'Ambrosio, 2022. "Human wellbeing and machine learning," CEP Discussion Papers dp1863, Centre for Economic Performance, LSE.
  97. 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).
  98. 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.
  99. 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.
  100. Engel, Christoph, 2020. "Estimating heterogeneous reactions to experimental treatments," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 124-147.
  101. 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.
  102. Dean Eckles & Maurits Kaptein, 2019. "Bootstrap Thompson Sampling and Sequential Decision Problems in the Behavioral Sciences," SAGE Open, , vol. 9(2), pages 21582440198, June.
  103. 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).
  104. 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.
  105. 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.
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  107. 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.
  108. 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.
  109. Yao Cui & Andrew M. Davis, 2022. "Tax-Induced Inequalities in the Sharing Economy," Management Science, INFORMS, vol. 68(10), pages 7202-7220, October.
  110. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
  111. 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.
  112. Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
  113. Andor, Mark A. & Gerster, Andreas & Peters, Jörg, 2022. "Information campaigns for residential energy conservation," European Economic Review, Elsevier, vol. 144(C).
  114. 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.
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  119. 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.
  120. Miller, Steve, 2020. "Causal forest estimation of heterogeneous and time-varying environmental policy effects," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
  121. 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.
  122. 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.
  123. 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.
  124. 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.
  125. Alex Armand & Britta Augsburg & Antonella Bancalari, 2021. "Coordination and the poor maintenance trap: an experiment on public infrastructure in India," NOVAFRICA Working Paper Series wp2110, Universidade Nova de Lisboa, Nova School of Business and Economics, NOVAFRICA.
  126. 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.
  127. Seojeong Lee & Youngki Shin, 2021. "Complete subset averaging with many instruments," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 290-314.
  128. 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.
  129. 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.
  130. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
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