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Efficient Propensity Score Regression Estimators of Multivalued Treatment Effects for the Treated

  • Date 2017-06-22 (Thu)
  • Time 10:30 AM
  • Venue Conference Room B110
  • Presider Professor Yu-Chin Hsu
  • Speaker Professor Ying-Ying Lee
  • Background Professor Lee received her Ph.D. from the University of Wisconsin-Madison in 2013. She is currently an assistant Professor of the University of California, Irvine. Her research interests include Econometric Theory, Empirical Microeconomics.
  • Abstract Matching is a widely-used program evaluation estimation method when treatment is assigned at random conditional on observable characteristics. When a multivalued treatment takes on more than two values, valid causal comparisons for a subpopulation who is treated a particular treatment level are based on two propensity scores — one for the treated level and one for the counterfactual level. The main contribution of this paper is propensity score regression estimators for a class of treatment effects for the treated that achieve the semi-parametric efficiency bounds under the cases when the propensity scores are unknown and when they are known. We derive the large sample distribution that reveals how first step estimation of the propensity score as generated regressors affects asymptotic efficiency. We contribute to the binary treatment literature by a new propensity score regression estimator for the average/quantile treatment effect for the treated: the proposed efficient estimator matches on a normalized propensity score that is a combination of the true propensity score and its nonparametric estimate. Moreover, we formally show that the semiparametric efficiency bound is reduced by knowledge of the propensity scores for the treated levels, but is not affected by knowledge of the propensity score for the counterfactual level. A Monte-Carlo experiment supports our theoretical findings.