Nonparametric Tests for Treatment Effect Heterogeneity with Duration Outcomes

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.

本文针对关注结局（通常为持续期变量）可能存在右删失的场景，提出了若干用于检验处理效应异质性的方法。所提检验旨在考察某政策是否满足以下两项条件：1）对所有基于协变量定义的子群体，其分布层面（平均层面）的效应均为零；2）不同子群体间的平均效应保持同质。该类检验基于两步Kaplan-Meier积分构建，无需依赖参数化分布假设、形状约束，亦无需对不同子群体间的潜在处理效应异质性施加任何约束。我们提出的分析框架不仅适用于外生处理分配场景，还可纳入处理不依从的情形——这在诸多实际应用中是一项关键特性。所提检验在固定备择假设下具备相合性，且能够以参数化$n^{-1/2}$速率检测收敛于原假设的非参数备择假设，其中$n$代表样本量。临界值可借助乘子Bootstrap（multiplier bootstrap）方法计算得到。本文通过蒙特卡洛（Monte Carlo）模拟研究，以及一项关于劳动力市场项目对失业持续期影响的实证应用，对所提检验的有限样本性质进行了检验。用于实现所有所提检验的开源软件已对外发布。