  In a unified framework, we provide estimators and confidence bands for a
variety of treatment effects when the outcome of interest, typically a
duration, is subjected to right censoring. Our methodology accommodates
average, distributional, and quantile treatment effects under different
identifying assumptions including unconfoundedness, local treatment effects,
and nonlinear differences-in-differences. The proposed estimators are easy to
implement, have close-form representation, are fully data-driven upon
estimation of nuisance parameters, and do not rely on parametric distributional
assumptions, shape restrictions, or on restricting the potential treatment
effect heterogeneity across different subpopulations. These treatment effects
results are obtained as a consequence of more general results on two-step
Kaplan-Meier estimators that are of independent interest: we provide conditions
for applying (i) uniform law of large numbers, (ii) functional central limit
theorems, and (iii) we prove the validity of the ordinary nonparametric
bootstrap in a two-step estimation procedure where the outcome of interest may
be randomly censored.
