Pedro H. C. Sant'Anna
arXiv 7 Dec 2016 · Statistics — Methodology · publishedJournal of Business and Economic Statistics (2020) · 14 citations (OpenAlex)
arXiv:1612.02090 · PDF · DOI · OpenAlex · Extracted main text
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 $n^{-1/2}$-rate, $n$ being the sample size. Critical values are computed with the assistance of a multiplier bootstrap. The finite sample properties of the proposed tests are examined by means of a Monte Carlo study and an application about the effect of labor market programs on unemployment duration. Open-source software is available for implementing all proposed tests.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Sant'Anna (2016) Program Evaluation with Right-Censored Data self | 1.000 | 5 | 4 | 100% |
| 2 | Hsu (2017) Consistent tests for conditional treatment effects | 1.000 | 5 | 3 | 100% |
| 3 | Crump, Hotz, Imbens \ Mitnik (2008) Nonparametric tests for treatment effect heterogeneity | 0.974 | 13 | 3 | 92% |
| 4 | Abadie (2002) Bootstrap tests for distributional treatment effects in instrumental variable models | 0.874 | 8 | 2 | 100% |
| 5 | Hirano, Imbens \ Ridder (2003) Efficient estimation of average treatment effects using the estimated propensity score | 0.843 | 4 | 4 | 75% |
| 6 | Escanciano (2006) Goodness-of-Fit Tests for Linear and Nonlinear Time Series Models | 0.843 | 4 | 3 | 75% |
| 7 | Andrews \ Shi (2013) Inference Based on Conditional Moment Inequalities | 0.811 | 4 | 2 | 100% |
| 8 | Rosenbaum \ Rubin (1983) The central role of the propensity score in observational studies for causal effects | 0.811 | 4 | 2 | 100% |
| 9 | Stute (1996) Distributional convergence under random censorship when covariables are present | 0.737 | 3 | 3 | 67% |
| 10 | Andrews \ Shi (2017) Inference based on many conditional moment inequalities | 0.737 | 3 | 2 | 100% |
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