Efficient semiparametric estimation of quantile treatment effects
TL;DR: This article developed estimators for quantile treatment effects under the identifying restriction that selection to treatment is based on observable characteristics, without requiring computation of the conditional quantiles of the potential outcomes.
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Abstract: This paper develops estimators for quantile treatment effects under the identifying restriction that selection to treatment is based on observable characteristics. Identification is achieved without requiring computation of the conditional quantiles of the potential outcomes. Instead, the identification results for the marginal quantiles lead to an estimation procedure for the quantile treatment effect parameters that has two steps: nonparametric estimation of the propensity score and computation of the difference between the solutions of two separate minimization problems. Root-N consistency, asymptotic normality, and achievement of the semiparametric efficiency bound are shown for that estimator. A consistent estimation procedure for the variance is also presented. Finally, the method developed here is applied to evaluation of a job training program and to a Monte Carlo exercise. Results from the empirical application indicate that the method works relatively well even for a data set with limited overlap between treated and controls in the support of covariates. The Monte Carlo study shows that, for a relatively small sample size, the method produces estimates with good precision and low bias, especially for middle quantiles.
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Figures
FIGURE 1: LaLonde/Dehejia and Wahba Experimental Data Set (Treatment: solid line; Control: dashed line) 
FIGURE 2: LaLonde/Dehejia and Wahba Non-Experimental Data Set (Treatment: solid line; Counterfactual Control: dashed line; Actual Control: dotted line) 
FIGURE 3: LaLonde/Dehejia and Wahba Non-Experimental Data Set 
FIGURE 5: LaLonde/Dehejia and Wahba Data Set Non-experimental and Experimental Controls 
TABLE 2: LaLonde/Dehejia and Wahba Data Set - Quantiles ofPotential Earnings
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References
The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Matching As An Econometric Evaluation Estimator
TL;DR: In this article, a rigorous distribution theory for kernel-based matching is presented, and the method of matching is extended to more general conditions than the ones assumed in the statistical literature on the topic.
The Economics and Econometrics of Active Labor Market Programs
TL;DR: In this paper, the authors examine the impacts of active labor market policies, such as job training, job search assistance, and job subsidies, and the methods used to evaluate their effectiveness.
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Nonparametrics: Statistical Methods Based on Ranks
Erich L. Lehmann,H. J. M. D'Abrera +1 more
- 01 Jan 1975
TL;DR: Rank Tests for Comparing Two Treatments and Blocked Comparisons for two Treatments in a Population Model and the One-Sample Problem as discussed by the authors were used to compare more than two treatments.
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