Estimation and Inference for Policy Relevant Treatment Effects

Abstract

Estimation of the policy relevant treatment effects (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, our work is the first to develop limit distribution theories for inference about the PRTE.

Welfare Analysis via Marginal Treatment Effects

“…The applicability of our representation theorem relies on the identification of the MTE from this literature. Finally, this paper also complements the literature on policy relevant treatment effects (e.g., Heckman and Vytlacil, 2001, 2005, 2007Brinch et al, 2017;Carneiro, Lokshin, and Umapathi, 2017;Mogstad et al, 2018;Sasaki and Ura, 2018) which develops methods of identification, estimation and inference for average welfare gains under counterfactual policies based on the MTE.…”