Program Evaluation with Right-Censored Data

40 Pages Posted: 14 Apr 2016  

Pedro H. C. Sant'Anna

Vanderbilt University - College of Arts and Science - Department of Economics

Date Written: April 9, 2016


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.

Keywords: Kaplan-Meier Integrals, Survival Analysis; Policy Evaluation, Treatment effects, Duration models

Suggested Citation

Sant'Anna, Pedro H. C., Program Evaluation with Right-Censored Data (April 9, 2016). Available at SSRN:

Pedro H. C. Sant'Anna (Contact Author)

Vanderbilt University - College of Arts and Science - Department of Economics ( email )

Box 1819 Station B
Nashville, TN 37235
United States

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