Double Machine Learning Based Program Evaluation under Unconfoundedness
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- 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.
- Michael C. Knaus, 2020. "Double Machine Learning based Program Evaluation under Unconfoundedness," Papers 2003.03191, arXiv.org, revised Jun 2022.
- 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.
References listed on IDEAS
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018.
"Double/debiased machine learning for treatment and structural parameters,"
Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers 28/17, Institute for Fiscal Studies.
- 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.
- 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," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
- 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.
- Michael Lechner, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Papers 1812.09487, arXiv.org, revised Jul 2019.
- Toru Kitagawa & Aleksey Tetenov, 2018.
"Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice,"
Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
- Toru Kitagawa & Aleksey Tetenov, 2015. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers CWP10/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Toru Kitagawa & Aleksey Tetenov, 2017. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers CWP24/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Toru Kitagawa & Aleksey Tetenov, 2015. "Who should be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Carlo Alberto Notebooks 402, Collegio Carlo Alberto.
- 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).
- 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.
- Lechner, Michael & Cockx, Bart & Bollens, Joost, 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 Dec 2022.
- 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 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).
- Lechner, Michael & Gerfin, Michael, 2001. "A Microeconometric Evaluation of Active Labour Market Policy in Switzerland," CEPR Discussion Papers 2993, C.E.P.R. Discussion Papers.
- Zhengyuan Zhou & Susan Athey & Stefan Wager, 2023.
"Offline Multi-Action Policy Learning: Generalization and Optimization,"
Operations Research, INFORMS, vol. 71(1), pages 148-183, January.
- Zhou, Zhengyuan & Athey, Susan & Wager, Stefan, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Research Papers 3734, Stanford University, Graduate School of Business.
- Zhengyuan Zhou & Susan Athey & Stefan Wager, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Papers 1810.04778, arXiv.org, revised Nov 2018.
- 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.
- 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).
- 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.
- Michael C. Knaus, 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," Papers 1805.10300, arXiv.org, revised Jan 2019.
- Knaus, Michael C., 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," IZA Discussion Papers 11547, Institute of Labor Economics (IZA).
- Isphording, Ingo E., 2014. "Language and Labor Market Success," IZA Discussion Papers 8572, Institute of Labor Economics (IZA).
- 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.
- Christopher R. Knittel & Samuel Stolper, 2019. "Using Machine Learning to Target Treatment: The Case of Household Energy Use," NBER Working Papers 26531, National Bureau of Economic Research, Inc.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
- David Card & Jochen Kluve & Andrea Weber, 2018.
"What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations,"
Journal of the European Economic Association, European Economic Association, vol. 16(3), pages 894-931.
- Card, David & Kluve, Jochen & Weber, Andrea, 2015. "What works? A meta analysis of recent active labor market program evaluations," Ruhr Economic Papers 572, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
- Card, David & Kluve, Jochen & Weber, Andrea, 2015. "What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations," IZA Discussion Papers 9236, Institute of Labor Economics (IZA).
- David Card & Jochen Kluve & Andrea Weber, 2015. "What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations," NBER Working Papers 21431, National Bureau of Economic Research, Inc.
- Andreas Gulyas & Krzysztof Pytka, 2019.
"Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach,"
CRC TR 224 Discussion Paper Series
crctr224_2019_131, University of Bonn and University of Mannheim, Germany.
- Pytka, Krzysztof & Gulyas, Andreas, 2021. "Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242402, Verein für Socialpolitik / German Economic Association.
- Andreas Gulyas & Krzysztof Pytka, 2020. "Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach," CRC TR 224 Discussion Paper Series crctr224_2020_131v2, University of Bonn and University of Mannheim, Germany.
- Lechner, Michael & Smith, Jeffrey, 2007.
"What is the value added by caseworkers?,"
Labour Economics, Elsevier, vol. 14(2), pages 135-151, April.
- Lechner, Michael & Smith, Jeffrey A., 2003. "What is the Value Added by Caseworkers?," IZA Discussion Papers 728, Institute of Labor Economics (IZA).
- Lechner, Michael & Smith, Jeffrey, 2003. "What is the Value Added by Caseworkers?," CEPR Discussion Papers 3825, C.E.P.R. Discussion Papers.
- Michael Lechner & Jeffrey Smith, 2003. "What is the Value Added by Caseworkers?," University of St. Gallen Department of Economics working paper series 2003 2003-05, Department of Economics, University of St. Gallen.
- Michael Lechner & Jeffrey Smith, 2003. "What is the Value Added by Caseworkers?," University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 20031, University of Western Ontario, Centre for Human Capital and Productivity (CHCP).
- Bruno Crépon & Gerard J. van den Berg, 2016.
"Active Labor Market Policies,"
Annual Review of Economics, Annual Reviews, vol. 8(1), pages 521-546, October.
- Crépon, Bruno & van den Berg, Gerard J., 2016. "Active labor market policies," Working Paper Series 2016:17, IFAU - Institute for Evaluation of Labour Market and Education Policy.
- Crépon, Bruno & van den Berg, Gerard J., 2016. "Active Labor Market Policies," IZA Discussion Papers 10321, Institute of Labor Economics (IZA).
- 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.
- 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," Economics Working Paper Series 1711, University of St. Gallen, School of Economics and Political Science.
- Lechner, Michael & Strittmatter, Anthony & Knaus, Michael C., 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," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
- 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.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," IZA Discussion Papers 12039, Institute of Labor Economics (IZA).
- Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Papers 1810.13237, arXiv.org, revised Dec 2018.
- Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
- Knaus, Michael C. & Lechner, Michael & anthony.strittmatter@unisg.ch, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Economics Working Paper Series 1817, University of St. Gallen, School of Economics and Political Science.
- 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.
- Susan Athey & Guido W. Imbens, 2017.
"The State of Applied Econometrics: Causality and Policy Evaluation,"
Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
- Susan Athey & Guido Imbens, 2016. "The State of Applied Econometrics - Causality and Policy Evaluation," Papers 1607.00699, arXiv.org.
- Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017.
"Generic machine learning inference on heterogenous treatment effects in randomized experiments,"
CeMMAP working papers
CWP61/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- 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.
- Farrell, Max H., 2015.
"Robust inference on average treatment effects with possibly more covariates than observations,"
Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
- Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
- Michael Lechner, 1999.
"Nonparametric bounds on employment and income effects of continuous vocational training in East Germany,"
Econometrics Journal, Royal Economic Society, vol. 2(1), pages 1-28.
- Lechner, Michael, 1996. "Nonparametric bounds on employment and income effects of continuous vocational training in East Germany," ZEW Discussion Papers 96-31, ZEW - Leibniz Centre for European Economic Research.
- Lechner, Michael, 1999. "Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 74-90, January.
- Keisuke Hirano & Jack R. Porter, 2009.
"Asymptotics for Statistical Treatment Rules,"
Econometrica, Econometric Society, vol. 77(5), pages 1683-1701, September.
- Hirano, Keisuke & Porter, Jack, 2006. "Asymptotics for statistical treatment rules," MPRA Paper 1173, University Library of Munich, Germany.
- 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 13/16, Institute for Fiscal Studies.
- 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.
- Jacob, Daniel & Härdle, Wolfgang Karl & Lessmann, Stefan, 2019. "Group Average Treatment Effects for Observational Studies," IRTG 1792 Discussion Papers 2019-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Susan Athey & Guido W. Imbens & Stefan Wager, 2018.
"Approximate residual balancing: debiased inference of average treatment effects in high dimensions,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
- Susan Athey & Guido W. Imbens & Stefan Wager, 2016. "Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions," Papers 1604.07125, arXiv.org, revised Jan 2018.
- Conny Wunsch, 2016. "How to minimize lock-in effects of programs for unemployed workers," IZA World of Labor, Institute of Labor Economics (IZA), pages 288-288, September.
- Stoye, Jörg, 2012. "Minimax regret treatment choice with covariates or with limited validity of experiments," Journal of Econometrics, Elsevier, vol. 166(1), pages 138-156.
- Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2022.
"Estimation of Conditional Average Treatment Effects With High-Dimensional Data,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 313-327, January.
- Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2019. "Estimation of Conditional Average Treatment Effects with High-Dimensional Data," Papers 1908.02399, arXiv.org, revised Jul 2021.
- Rafael Lalive & Jan C. van Ours & Josef Zweimueller, "undated". "The Impact of Active Labor Market Programs on the Duration of Unemployment," IEW - Working Papers 041, Institute for Empirical Research in Economics - University of Zurich.
- Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018.
"The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages,"
Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
- Victor Chernozhukov & Ivan Fernandez-Val & Ye Luo, 2015. "The sorted effects method: discovering heterogeneous effects beyond their averages," CeMMAP working papers 74/15, Institute for Fiscal Studies.
- Victor Chernozhukov & Ivan Fernandez-Val & Ye Luo, 2015. "The sorted effects method: discovering heterogeneous effects beyond their averages," CeMMAP working papers CWP74/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Ivan Fernandez-Val & Ye Luo, 2015. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Papers 1512.05635, arXiv.org, revised May 2018.
- van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
- Charles F. Manski, 2004.
"Statistical Treatment Rules for Heterogeneous Populations,"
Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
- Charles F. Manski, 2003. "Statistical treatment rules for heterogeneous populations," CeMMAP working papers CWP03/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Charles F. Manski, 2003. "Statistical treatment rules for heterogeneous populations," CeMMAP working papers 03/03, Institute for Fiscal Studies.
- Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
- Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
- 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.
- Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
- Athey, Susan & Wager, Stefan, 2017. "Efficient Policy Learning," Research Papers 3506, Stanford University, Graduate School of Business.
- Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
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- Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2019. "Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germany's Programmes for Long Term Unemployed," IZA Discussion Papers 12526, Institute of Labor Economics (IZA).
- 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," IAB-Discussion Paper 202005, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
- 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.
- 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).
- 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.
- Lechner, Michael & Strittmatter, Anthony & Knaus, Michael C., 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.
- Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
- Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
- 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," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
- 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.
- Michael Lechner, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Papers 1812.09487, arXiv.org, revised Jul 2019.
- 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.
- Knaus, Michael C., 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," IZA Discussion Papers 11547, Institute of Labor Economics (IZA).
- Michael C. Knaus, 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," Papers 1805.10300, arXiv.org, revised Jan 2019.
- 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.
- Heiler, Phillip & Knaus, Michael C., 2022. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," IZA Discussion Papers 15580, Institute of Labor Economics (IZA).
- 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.
- 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).
More about this item
Keywords
causal machine learning; individualized treatment rules; conditional average treatment effects; optimal policy learning; multiple treatments;All these keywords.
JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2020-04-13 (Big Data)
- NEP-CMP-2020-04-13 (Computational Economics)
- NEP-EXP-2020-04-13 (Experimental Economics)
Statistics
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