  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.
