Doubly robust dose-response estimation for continuous treatments via generalized propensity score augmented outcome regression

TL;DR

This paper introduces a doubly robust method for estimating continuous dose-response relationships that combines outcome regression with inverse propensity score augmentation, providing accurate estimates even under model misspecification.

Contribution

It proposes a novel doubly robust estimator for continuous treatments that integrates outcome regression with generalized propensity scores, enhancing robustness and accuracy.

Findings

Performs well under model misspecification

Provides consistent estimates of dose-response functions

Achieves good finite-sample efficiency

Abstract

This paper constructs a doubly robust estimator for continuous dose-response estimation. An outcome regression model is augmented with a set of inverse generalized propensity score covariates to correct for potential misspecification bias. From the augmented model we can obtain consistent estimates of mean average potential outcomes for distinct strata of the treatment. A polynomial regression is then fitted to these point estimates to derive a Taylor approximation to the continuous dose-response function. The bootstrap is used for variance estimation. Analytical results and simulations show that our approach can provide a good approximation to linear or nonlinear dose-response functions under various sources of misspecification of the outcome regression or propensity score models. Efficiency in finite samples is good relative to minimum variance consistent estimators.