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Estimation and Inference for Policy Relevant Treatment Effects

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  • Yuya Sasaki
  • Takuya Ura

Abstract

The policy relevant treatment effect (PRTE) measures the average effect of switching from a status-quo policy to a counterfactual policy. Estimation of the PRTE involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects. These preliminary estimators can affect the asymptotic distribution of the PRTE estimator in complicated and intractable manners. In this light, we propose an orthogonal score for double debiased estimation of the PRTE, whereby the asymptotic distribution of the PRTE estimator is obtained without any influence of preliminary parameter estimators as far as they satisfy mild requirements of convergence rates. To our knowledge, this paper is the first to develop limit distribution theories for inference about the PRTE.

Suggested Citation

  • Yuya Sasaki & Takuya Ura, 2018. "Estimation and Inference for Policy Relevant Treatment Effects," Papers 1805.11503, arXiv.org, revised Jul 2020.
    • Handle: RePEc:arx:papers:1805.11503
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    References listed on IDEAS

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    Cited by:

    1. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.

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