Estimation and Inference for Policy Relevant Treatment Effects

TL;DR

This paper introduces a novel orthogonal score method for estimating the policy relevant treatment effect (PRTE), enabling more accurate inference by mitigating the influence of preliminary estimators.

Contribution

It develops the first limit distribution theory for PRTE inference using double debiased estimation with orthogonal scores.

Findings

Orthogonal score approach reduces bias from preliminary estimators.

Asymptotic distribution of PRTE estimator is derived.

Method improves robustness of policy effect estimation.

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.