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On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects

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Cited by:

  1. James J. Heckman & Petra E. Todd, 2009. "A note on adapting propensity score matching and selection models to choice based samples," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 230-234, January.
  2. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2008. "Nonparametric Tests for Treatment Effect Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 389-405, August.
  3. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
  4. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org.
  5. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
  6. Chabé-Ferret, Sylvain, 2015. "Analysis of the bias of Matching and Difference-in-Difference under alternative earnings and selection processes," Journal of Econometrics, Elsevier, vol. 185(1), pages 110-123.
  7. Kyoo il Kim, 2019. "Efficiency of Average Treatment Effect Estimation When the True Propensity Is Parametric," Econometrics, MDPI, Open Access Journal, vol. 7(2), pages 1-13, May.
  8. 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.
  9. Cuong NGUYEN, 2016. "An Introduction to Alternative Methods in Program Impact Evaluation," Journal of Economic and Social Thought, KSP Journals, vol. 3(3), pages 349-375, September.
  10. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
  11. Cansino, José M. & Lopez-Melendo, Jaime & Pablo-Romero, María del P. & Sánchez-Braza, Antonio, 2013. "An economic evaluation of public programs for internationalization: The case of the Diagnostic program in Spain," Evaluation and Program Planning, Elsevier, vol. 41(C), pages 38-46.
  12. Wang, Qihua & Lai, Peng, 2011. "Empirical likelihood calibration estimation for the median treatment difference in observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1596-1609, April.
  13. John C. Ham & Xianghong Li & Patricia B. Reagan, 2004. "Propensity Score Matching, a Distance-Based Measure of Migration, and the Wage Growth of Young Men," IEPR Working Papers 05.13, Institute of Economic Policy Research (IEPR).
  14. Susan Athey & Guido W. Imbens, 2006. "Identification and Inference in Nonlinear Difference-in-Differences Models," Econometrica, Econometric Society, vol. 74(2), pages 431-497, March.
  15. Bryan S. Graham & Guido W. Imbens & Geert Ridder, 2020. "Identification and Efficiency Bounds for the Average Match Function Under Conditionally Exogenous Matching," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 303-316, April.
  16. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
  17. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
  18. Lenis, David & Ackerman, Benjamin & Stuart, Elizabeth A., 2018. "Measuring model misspecification: Application to propensity score methods with complex survey data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 48-57.
  19. Schnitzer Mireille E. & Lok Judith J. & Gruber Susan, 2016. "Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 97-115, May.
  20. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls," Papers 1201.0224, arXiv.org, revised May 2012.
  21. Jochen Kluve & Hartmut Lehmann & Christoph M. Schmidt, 2000. "Disentangling Treatment Effects of Polish Active Labour Market Policies: Evidence from Matched Samples," CERT Discussion Papers 0007, Centre for Economic Reform and Transformation, Heriot Watt University.
  22. Susan Athey & Guido Imbens & Thai Pham & Stefan Wager, 2017. "Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges," American Economic Review, American Economic Association, vol. 107(5), pages 278-281, May.
  23. Nguyen Viet, Cuong, 2006. "An Introduction to Alternative Methods in Program Impact Evaluation," MPRA Paper 24900, University Library of Munich, Germany.
  24. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference in Case-Control Studies," Papers 2004.08318, arXiv.org.
  25. Guido W. Imbens, 1999. "The Role of the Propensity Score in Estimating Dose-Response Functions," NBER Technical Working Papers 0237, National Bureau of Economic Research, Inc.
  26. Mammen, Enno & Rothe, Christoph & Schienle, Melanie, 2016. "Semiparametric Estimation With Generated Covariates," Econometric Theory, Cambridge University Press, vol. 32(5), pages 1140-1177, October.
  27. Jinyong Hahn & Keisuke Hirano & Dean Karlan, 2011. "Adaptive Experimental Design Using the Propensity Score," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 96-108, January.
  28. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Dec 2017.
  29. Taisuke Otsu & Chen Qiu, 2018. "Information theoretic approach to high dimensional multiplicative models: Stochastic discount factor and treatment effect," STICERD - Econometrics Paper Series 595, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  30. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
  31. Bryan S. Graham, 2011. "Efficiency Bounds for Missing Data Models With Semiparametric Restrictions," Econometrica, Econometric Society, vol. 79(2), pages 437-452, March.
  32. Sokbae (Simon) Lee & Yoon-Jae Whang, 2009. "Nonparametric tests of conditional treatment effects," CeMMAP working papers CWP36/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  33. Athey, Susan & Wager, Stefan, 2017. "Efficient Policy Learning," Research Papers 3506, Stanford University, Graduate School of Business.
  34. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
  35. Ying-Ying Lee, 2015. "Efficient propensity score regression estimators of multi-valued treatment effects for the treated," Economics Series Working Papers 738, University of Oxford, Department of Economics.
  36. Samuel D. Lendle & Meenakshi S. Subbaraman & Mark J. van der Laan, 2013. "Identification and Efficient Estimation of the Natural Direct Effect among the Untreated," Biometrics, The International Biometric Society, vol. 69(2), pages 310-317, June.
  37. Zeqin Liu & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Statistical Analysis and Evaluation of Macroeconomic Policies: A Selective Review," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201904, University of Kansas, Department of Economics, revised Mar 2019.
  38. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
  39. 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.
  40. Michael Zimmert, 2018. "Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding," Papers 1809.01643, arXiv.org, revised Jul 2019.
  41. Stefan Tubbicke, 2020. "Entropy Balancing for Continuous Treatments," Papers 2001.06281, arXiv.org, revised May 2020.
  42. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2016. "Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 288-301, April.
  43. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
  44. Wang, Qihua & Härdle, Wolfgang & Linton, Oliver, 2002. "Semiparametric regression analysis under imputation for missing response data," SFB 373 Discussion Papers 2002,6, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  45. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2018. "A Unified Framework for Efficient Estimation of General Treatment Models," Papers 1808.04936, arXiv.org, revised Aug 2018.
  46. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
  47. Fan, Yanqin & Guerre, Emmanuel & Zhu, Dongming, 2017. "Partial identification of functionals of the joint distribution of “potential outcomes”," Journal of Econometrics, Elsevier, vol. 197(1), pages 42-59.
  48. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
  49. 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).
  50. Flores, Carlos A. & Flores-Lagunes, Alfonso, 2009. "Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness," IZA Discussion Papers 4237, Institute of Labor Economics (IZA).
  51. Frölich, Markus & Michaelowa, Katharina, 2004. "Peer effects and textbooks in primary education: Evidence from francophone sub-Saharan Africa," HWWA Discussion Papers 311, Hamburg Institute of International Economics (HWWA).
  52. Jesse Rothstein & Albert Yoon, 2006. "Mismatch in Law School," Working Papers 29, Princeton University, Woodrow Wilson School of Public and International Affairs, Education Research Section..
  53. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
  54. Takeshima, Hiroyuki & Shrestha, Rudra Bahadur & Kaphle, Basu Dev & Karkee, Madhab & Pokhrel, Suroj & Kumar, Anjani, 2016. "Effects of agricultural mechanization on smallholders and their self-selection into farming: An insight from the Nepal Terai," IFPRI discussion papers 1583, International Food Policy Research Institute (IFPRI).
  55. Bryan S. Graham & Cristine Campos de Xavier Pinto, 2018. "Semiparametrically Efficient Estimation of the Average Linear Regression Function," NBER Working Papers 25234, National Bureau of Economic Research, Inc.
  56. Lechner, Michael, 2004. "Sequential Matching Estimation of Dynamic Causal Models," IZA Discussion Papers 1042, Institute of Labor Economics (IZA).
  57. Sookyo Jeong & Hongseok Namkoong, 2020. "Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects," Papers 2007.02411, arXiv.org, revised Jul 2020.
  58. 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.
  59. Rothe, Christoph, 2016. "The Value of Knowing the Propensity Score for Estimating Average Treatment Effects," IZA Discussion Papers 9989, Institute of Labor Economics (IZA).
  60. Arthur Lewbel, 2007. "Estimation of Average Treatment Effects with Misclassification," Econometrica, Econometric Society, vol. 75(2), pages 537-551, March.
  61. Fan, Yanqin & Park, Sang Soo, 2009. "Partial identification of the distribution of treatment effects and its confidence sets," MPRA Paper 37148, University Library of Munich, Germany.
  62. Pohlmeier, Winfried & Seiberlich, Ruben & Uysal, Selver Derya, 2016. "A simple and successful shrinkage method for weighting estimators of treatment effects," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 512-525.
  63. Francesco Bravo & David Jacho-Chavez, 2011. "Empirical Likelihood for Efficient Semiparametric Average Treatment Effects," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 1-24.
  64. Sergio Firpo & Cristine Pinto, 2016. "Identification and Estimation of Distributional Impacts of Interventions Using Changes in Inequality Measures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 457-486, April.
  65. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.
  66. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
  67. Hong, Han & Nekipelov, Denis, 2012. "Efficient local IV estimation of an empirical auction model," Journal of Econometrics, Elsevier, vol. 168(1), pages 60-69.
  68. Isaiah Andrews & Emily Oster, 2017. "Weighting for External Validity," NBER Working Papers 23826, National Bureau of Economic Research, Inc.
  69. Romina Cavatassi & Mario González & Paul Winters & Jorge Andrade-Piedra & Graham Thiele & Patricio Espinosa, 2009. "Linking Smallholders to the New Agricultural Economy: An Evaluation of the Plataformas Program in Ecuador," Working Papers 09-06, Agricultural and Development Economics Division of the Food and Agriculture Organization of the United Nations (FAO - ESA).
  70. Andrew Chesher & Erich Battistin, 2004. "The Impact of Measurement Error on Evaluation Methods Based on Strong Ignorability," Econometric Society 2004 North American Summer Meetings 339, Econometric Society.
  71. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
  72. James R. Meldrum, 2016. "Floodplain Price Impacts by Property Type in Boulder County, Colorado: Condominiums Versus Standalone Properties," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(4), pages 725-750, August.
  73. Kyungchul Song, 2009. "Efficient Estimation of Average Treatment Effects under Treatment-Based Sampling," PIER Working Paper Archive 09-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  74. Wang-Sheng Lee, 2013. "Propensity score matching and variations on the balancing test," Empirical Economics, Springer, vol. 44(1), pages 47-80, February.
  75. Shengfang Tang & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Testing Unconfoundedness Assumption Using Auxiliary Variables," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201905, University of Kansas, Department of Economics, revised Mar 2019.
  76. Francesco Bravo, 2013. "Partially linear varying coefficient models with missing at random responses," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 721-762, August.
  77. Andrew Bennett & Nathan Kallus, 2020. "Efficient Policy Learning from Surrogate-Loss Classification Reductions," Papers 2002.05153, arXiv.org.
  78. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
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  80. Halbert White & Karim Chalak, 2013. "Identification and Identification Failure for Treatment Effects Using Structural Systems," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 273-317, November.
  81. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
  82. Tarozzi, Alessandro, 2007. "Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 314-336, July.
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  85. Richard Blundell & Monica Costa Dias, 2000. "Evaluation methods for non-experimental data," Fiscal Studies, Institute for Fiscal Studies, vol. 21(4), pages 427-468, January.
  86. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
  87. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
  88. Denis Conniffe & Vanessa Gash & Philip J. O'Connell, 2000. "Evaluating State Programmes - “Natural Experiments” and Propensity Scores," The Economic and Social Review, Economic and Social Studies, vol. 31(4), pages 283-308.
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  93. Xun Lu, 2015. "A Covariate Selection Criterion for Estimation of Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 506-522, October.
  94. Roberto ESPOSTI, 2014. "To match, not to match, how to match: Estimating the farm-level impact of the CAP-first pillar reform (or: How to Apply Treatment-Effect Econometrics when the Real World is;a Mess)," Working Papers 403, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
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