Modified Causal Forests for Estimating Heterogeneous Causal Effects
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- Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
- Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
- Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Economics Working Paper Series 1901, University of St. Gallen, School of Economics and Political Science.
References listed on IDEAS
- Michael Lechner & Conny Wunsch, 2011. "Sensitivity of Matching-Based Program Evaluations to the Availability of Control Variables," CESifo Working Paper Series 3381, CESifo.
- Arun Advani & Tymon Sloczynski, 2013.
"Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies,"
CeMMAP working papers
CWP64/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Advani, Arun & Kitagawa, Toru & Sloczynski, Tymon, 2018. "Mostly Harmless Simulations? On the Internal Validity of Empirical Monte Carlo Studies," IZA Discussion Papers 11862, Institute of Labor Economics (IZA).
- Arun Advani & Toru Kitagawa & Tymon Sloczynski, 2018. "Mostly Harmless Simulations? On the Internal Validity of Empirical Monte Carlo Studies," Working Papers 124, Brandeis University, Department of Economics and International Businesss School.
- Arun Advani & Toru Kitagawa & Tymon Sloczynski, 2018. "Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies," CeMMAP working papers CWP56/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Advani, Arun & Sloczynski, Tymon, 2013. "Mostly Harmless Simulations? On the Internal Validity of Empirical Monte Carlo Studies," IZA Discussion Papers 7874, Institute of Labor Economics (IZA).
- Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017.
"Doubly robust uniform confidence band for the conditional average treatment effect function,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
- Sokbae (Simon) Lee & Ryo Okui & Yoon-Jae Whang, 2016. "Doubly robust uniform confidence band for the conditional average treatment effect function," CeMMAP working papers CWP03/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Lee, Sokbae & Okui, Ryo & Whang, Yoon-Jae, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," LSE Research Online Documents on Economics 86852, London School of Economics and Political Science, LSE Library.
- Sokbae Lee & Ryo Okui & Yoon-Jae Whang, 2016. "Doubly Robust Uniform Confidence Band For The Conditional Average Treatment Effect Function," KIER Working Papers 931, Kyoto University, Institute of Economic Research.
- Sokbae Lee & Ryo Okui & Yoon-Jae Whang, 2016. "Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function," Papers 1601.02801, arXiv.org, revised Oct 2016.
- Lechner, Michael & Wunsch, Conny, 2013.
"Sensitivity of matching-based program evaluations to the availability of control variables,"
Labour Economics, Elsevier, vol. 21(C), pages 111-121.
- Lechner, Michael & Wunsch, Conny, 2011. "Sensitivity of matching-based program evaluations to the availability of control variables," CEPR Discussion Papers 8294, C.E.P.R. Discussion Papers.
- Lechner, Michael & Wunsch, Conny, 2011. "Sensitivity of matching-based program evaluations to the availability of control variables," Economics Working Paper Series 1105, University of St. Gallen, School of Economics and Political Science.
- Lechner, Michael & Wunsch, Conny, 2011. "Sensitivity of Matching-Based Program Evaluations to the Availability of Control Variables," IZA Discussion Papers 5553, Institute of Labor Economics (IZA).
- Stefan Wager & Susan Athey, 2018.
"Estimation and Inference of Heterogeneous Treatment Effects using Random Forests,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
- Wager, Stefan & Athey, Susan, 2017. "Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests," Research Papers 3576, Stanford University, Graduate School of Business.
- 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 Mar 2019.
- Strittmatter, Anthony, 2019. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," GLO Discussion Paper Series 336, Global Labor Organization (GLO).
- Strittmatter, Anthony, 2019. "What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203499, Verein für Socialpolitik / German Economic Association.
- Vikas Ramachandra, 2018. "Deep Learning for Causal Inference," Papers 1803.00149, arXiv.org.
- Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016.
"Post-Selection Inference for Generalized Linear Models With Many Controls,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
- Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Post-Selection Inference for Generalized Linear Models with Many Controls," Papers 1304.3969, arXiv.org, revised Mar 2016.
- Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
- Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2015.
"Estimating Conditional Average Treatment Effects,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 485-505, October.
- Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2012. "Estimating Conditional Average Treatment Effects," CEU Working Papers 2012_16, Department of Economics, Central European University, revised 20 Jul 2012.
- Michael Lechner & Anthony Strittmatter, 2019.
"Practical procedures to deal with common support problems in matching estimation,"
Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 193-207, February.
- Lechner, Michael & Strittmatter, Anthony, 2014. "Practical Procedures to Deal with Common Support Problems in Matching Estimation," Economics Working Paper Series 1410, University of St. Gallen, School of Economics and Political Science.
- Lechner, Michael & Strittmatter, Anthony, 2017. "Practical Procedures to Deal with Common Support Problems in Matching Estimation," IZA Discussion Papers 10532, Institute of Labor Economics (IZA).
- Shuai Chen & Lu Tian & Tianxi Cai & Menggang Yu, 2017. "A general statistical framework for subgroup identification and comparative treatment scoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1199-1209, December.
- Michael Gerfin & Michael Lechner, 2002.
"A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland,"
Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
- Gerfin, Michael & Lechner, Michael, 2000. "Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," IZA Discussion Papers 154, Institute of Labor Economics (IZA).
- Gerfin, Michael & Lechner, Michael, 2001. "A Microeconometric Evaluation of Active Labour Market Policy in Switzerland," CEPR Discussion Papers 2993, C.E.P.R. Discussion Papers.
- Michael Knaus & Michael Lechner & Anthony Strittmatter, 2017.
"Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach,"
Papers
1709.10279, arXiv.org, revised May 2018.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," CEPR Discussion Papers 12224, C.E.P.R. Discussion Papers.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Economics Working Paper Series 1711, University of St. Gallen, School of Economics and Political Science.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
- A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017.
"Program Evaluation and Causal Inference With High‐Dimensional Data,"
Econometrica, Econometric Society, vol. 85, pages 233-298, January.
- Alexandre Belloni & Victor Chernozhukov & Ivan Fern'andez-Val & Christian Hansen, 2013. "Program Evaluation and Causal Inference with High-Dimensional Data," Papers 1311.2645, arXiv.org, revised Jan 2018.
- Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers CWP13/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Arun Advani & Toru Kitagawa & Tymon Słoczyński, 2019.
"Mostly harmless simulations? Using Monte Carlo studies for estimator selection,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 893-910, September.
- Arun Advani & Toru Kitagawa & Tymon S{l}oczy'nski, 2018. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," Papers 1809.09527, arXiv.org, revised Apr 2019.
- Advani, Arun & Kitagawa, Toru & Słoczyński, Tymon, 2019. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," The Warwick Economics Research Paper Series (TWERPS) 1192, University of Warwick, Department of Economics.
- Advani, Arun & Kitagawa, Toru & Sloczynski, Tymon, 2019. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," CAGE Online Working Paper Series 411, Competitive Advantage in the Global Economy (CAGE).
- Xinkun Nie & Stefan Wager, 2017. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects," Papers 1712.04912, arXiv.org, revised Aug 2020.
- Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
- Matt Taddy & Matt Gardner & Liyun Chen & David Draper, 2016. "A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 661-672, October.
- Guido W. Imbens, 2004.
"Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review,"
The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
- Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
- Lu Tian & Ash A. Alizadeh & Andrew J. Gentles & Robert Tibshirani, 2014. "A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1517-1532, December.
- Martin Huber & Michael Lechner & Giovanni Mellace, 2017.
"Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism,"
The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 180-183, March.
- Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "Why do tougher caseworkers increase employment? The role of programme assignment as a causal mechanism," Economics Working Paper Series 1414, University of St. Gallen, School of Economics and Political Science.
- Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
Citations
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Cited by:
- 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.
- Knaus, Michael C., 2020.
"Double Machine Learning based Program Evaluation under Unconfoundedness,"
Economics Working Paper Series
2004, University of St. Gallen, School of Economics and Political Science.
- Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, Institute of Labor Economics (IZA).
- Michael C. Knaus, 2020. "Double Machine Learning based Program Evaluation under Unconfoundedness," Papers 2003.03191, arXiv.org, revised Oct 2020.
- Michael Lechner & Gabriel Okasa, 2019.
"Random Forest Estimation of the Ordered Choice Model,"
Papers
1907.02436, arXiv.org, revised Jan 2020.
- 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.
- Cockx, Bart & Lechner, Michael & Bollens, Joost, 2019.
"Priority to Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium,"
IZA Discussion Papers
12875, Institute of Labor Economics (IZA).
- Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," ROA Research Memorandum 006, Maastricht University, Research Centre for Education and the Labour Market (ROA).
- Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority of Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium," CESifo Working Paper Series 8297, CESifo.
- Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Research Memorandum 015, Maastricht University, Graduate School of Business and Economics (GSBE).
- Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 20/998, Ghent University, Faculty of Economics and Business Administration.
- Boolens, Joost & Cockx, Bart & Lechner, Michael, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," CEPR Discussion Papers 14270, C.E.P.R. Discussion Papers.
- Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Economics Working Paper Series 2001, University of St. Gallen, School of Economics and Political Science.
- Bart Cockx & Michael Lechner & Joost Bollens, 2019. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Papers 1912.12864, arXiv.org, revised May 2020.
- Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," LIDAM Discussion Papers IRES 2020016, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
- Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
More about this item
JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy
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