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Nonparametric Tests for Treatment Effect Heterogeneity with Duration Outcomes


  • Pedro H. C. Sant'Anna


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.

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  • Pedro H. C. Sant'Anna, 2016. "Nonparametric Tests for Treatment Effect Heterogeneity with Duration Outcomes," Papers 1612.02090,, revised Feb 2020.
  • Handle: RePEc:arx:papers:1612.02090

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    1. Kevin J. Anstrom & Anastasios A. Tsiatis, 2001. "Utilizing Propensity Scores to Estimate Causal Treatment Effects with Censored Time-Lagged Data," Biometrics, The International Biometric Society, vol. 57(4), pages 1207-1218, December.
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    4. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2008. "Nonparametric Tests for Treatment Effect Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 389-405, August.
    5. Minsu Chang & Sokbae Lee & Yoon‐Jae Whang, 2015. "Nonparametric tests of conditional treatment effects with an application to single‐sex schooling on academic achievements," Econometrics Journal, Royal Economic Society, vol. 18(3), pages 307-346, October.
    6. Woodbury, Stephen A & Spiegelman, Robert G, 1987. "Bonuses to Workers and Employers to Reduce Unemployment: Randomized Trials in Illinois," American Economic Review, American Economic Association, vol. 77(4), pages 513-530, September.
    7. Herman J. Bierens & Werner Ploberger, 1997. "Asymptotic Theory of Integrated Conditional Moment Tests," Econometrica, Econometric Society, vol. 65(5), pages 1129-1152, September.
    8. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
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    10. Sang-jun Lee & Myoung-jae Lee, 2005. "Analysis of job-training effects on Korean women," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 549-562.
    11. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    12. Beare, Brendan K. & Moon, Jong-Myun, 2015. "Nonparametric Tests Of Density Ratio Ordering," Econometric Theory, Cambridge University Press, vol. 31(3), pages 471-492, June.
    13. Joseph Hotz, V. & Imbens, Guido W. & Mortimer, Julie H., 2005. "Predicting the efficacy of future training programs using past experiences at other locations," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 241-270.
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