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Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes
| Content Provider | Scilit |
|---|---|
| Author | Sant’Anna, Pedro H. C. |
| Copyright Year | 2020 |
| Description | This article proposes different tests for treatment effect heterogeneity when the outcome of interest, typically a duration variable, may be right-censored. The proposed tests study whether a policy 1) has zero distributional (average) effect for all subpopulations defined by covariate values, and 2) has homogeneous average effect across different subpopulations. The proposed tests are based on two-step Kaplan-Meier integrals and do not rely on parametric distributional assumptions, shape restrictions, or on restricting the potential treatment effect heterogeneity across different subpopulations. Our framework is suitable not only to exogenous treatment allocation but can also account for treatment noncompliance - an important feature in many applications. The proposed tests are consistent against fixed alternatives, and can detect nonparametric alternatives converging to the null at the parametric |
| Related Links | http://arxiv.org/pdf/1612.02090 |
| Ending Page | 832 |
| Page Count | 17 |
| Starting Page | 816 |
| ISSN | 07350015 |
| e-ISSN | 15372707 |
| DOI | 10.1080/07350015.2020.1737080 |
| Journal | Journal of Business & Economic Statistics |
| Issue Number | 3 |
| Volume Number | 39 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-07-03 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Business & Economic Statistics Mathematical Social Sciences Statistics and Probability Causal Inference Duration Data Empirical Process Kaplan-meier Survival Analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Statistics and Probability Social Sciences Economics and Econometrics Statistics, Probability and Uncertainty |
