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Non-parametric tests for distributional treatment effect for randomly censored responses


  • Myoung-jae Lee


For a binary treatment "ν"=0, 1 and the corresponding 'potential response'"Y"-super-0 for the control group ("ν"=0) and "Y"-super-1 for the treatment group ("ν"=1), one definition of no treatment effect is that "Y"-super-0 and "Y"-super-1 follow the same distribution given a covariate vector "X". Koul and Schick have provided a non-parametric test for no distributional effect when the realized response (1 - "ν")"Y"-super-0+"ν""Y"-super-1 is fully observed and the distribution of "X" is the same across the two groups. This test is thus not applicable to censored responses, nor to non-experimental (i.e. observational) studies that entail different distributions of "X" across the two groups. We propose '"X"-matched' non-parametric tests generalizing the test of Koul and Schick following an idea of Gehan. Our tests are applicable to non-experimental data with randomly censored responses. In addition to these motivations, the tests have several advantages. First, they have the intuitive appeal of comparing all available pairs across the treatment and control groups, instead of selecting a number of matched controls (or treated) in the usual pair or multiple matching. Second, whereas most matching estimators or tests have a non-overlapping support (of "X") problem across the two groups, our tests have a built-in protection against the problem. Third, Gehan's idea allows the tests to make good use of censored observations. A simulation study is conducted, and an empirical illustration for a job training effect on the duration of unemployment is provided. Copyright (c) 2009 Royal Statistical Society.

Suggested Citation

  • Myoung-jae Lee, 2009. "Non-parametric tests for distributional treatment effect for randomly censored responses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 243-264.
  • Handle: RePEc:bla:jorssb:v:71:y:2009:i:1:p:243-264

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    Cited by:

    1. Sokbae (Simon) Lee & Yoon-Jae Whang, 2009. "Nonparametric tests of conditional treatment effects," CeMMAP working papers CWP36/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Yanchun Jin, 2016. "Nonparametric tests for the effect of treatment on conditional variance," KIER Working Papers 948, Kyoto University, Institute of Economic Research.
    3. Maier, Michael, 2011. "Tests for distributional treatment effects under unconfoundedness," Economics Letters, Elsevier, vol. 110(1), pages 49-51, January.

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