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, <i>n</i> 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.

本文针对感兴趣的结局变量（通常为时长型变量）存在右删失的情形，提出了若干用于检验处理效应异质性（Treatment Effect Heterogeneity）的方法。所提检验旨在验证两项内容：(1) 针对所有由协变量（Covariate）取值定义的子群体，政策的分布（平均）效应均为零；(2) 不同子群体间的平均效应保持同质。所提检验基于两步Kaplan–Meier积分（Kaplan–Meier Integral）构建，无需依赖参数化分布假设、形状约束，亦无需限定不同子群体间潜在处理效应的异质性形式。本研究框架不仅适用于外生性处理分配（Exogenous Treatment Allocation）场景，还可兼容处理不依从（Treatment Noncompliance）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一致性，且能够以参数化的n⁻¹/²速率检测收敛于原假设的非参数备择假设，其中n为样本量。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）模拟实验，以及一项关于劳动力市场项目对失业时长影响的实证应用，考察了所提检验的有限样本性质。所有所提检验的实现代码均已开源，可供直接调用。