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Testing for the presence of treatment effect under selection on observables

Author

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  • Pier Luigi Conti

    (Sapienza Università di Roma)

  • Livia Giovanni

    (LUISS University)

Abstract

The evaluation of the possible effects of a treatment on an outcome plays a central role in both theoretical and applied statistical and econometrical literature. This paper focuses on nonparametric tests for possible difference in the distribution of potential outcomes due to receiving or not receiving a treatment. The approach is based on weighting observed data on the basis on the estimated propensity score. Kolmogorov–Smirnov type and Wilcoxon–Mann–Whitney type tests are constructed, and their limiting distributions are studied. Rejection regions are obtained by inverting confidence intervals. This involves the study of appropriate estimators of the limiting variance of test statistics. Approximations of quantiles via subsampling are also considered. The merits of the different tests are studied by Monte Carlo simulation. An application to the construction of tests for stochastic dominance is provided.

Suggested Citation

  • Pier Luigi Conti & Livia Giovanni, 2023. "Testing for the presence of treatment effect under selection on observables," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(4), pages 641-669, December.
    • Handle: RePEc:spr:alstar:v:107:y:2023:i:4:d:10.1007_s10182-022-00454-8
    DOI: 10.1007/s10182-022-00454-8
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    References listed on IDEAS

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    6. 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.
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