Nonparametric tests for treatment effect heterogeneity

• 成果类型: Article
• 署名作者: Crump, Richard K.; Hotz, V. Joseph; Imbens, Guido W.; Mitnik, Oscar A.
• 署名单位: University of California System; University of California Berkeley; Duke University; National Bureau of Economic Research; Harvard University; University of Miami; IZA Institute Labor Economics
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest.90.3.389
• 发表日期: 2008-08
• 页码: 389-405
• 关键词: efficient semiparametric estimation; propensity score; MODEL; PROGRAMS

摘要

In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, that is, that there is no heterogeneity in average treatment effects by covariates. We derive tests that are straightforward to implement and illustrate the use of these tests on data from two sets of experimental evaluations of the effects of welfare-to-work programs.