Double-robust and efficient methods for estimating the causal effects of a binary treatment

TL;DR

This paper introduces a new double-robust estimator for the average treatment effect from observational data, addressing issues with non-overlapping propensity scores and high-dimensional covariates, and proposes a novel model selection method.

Contribution

The paper presents a novel, locally efficient double-robust estimator that estimates treatment effects in various subpopulations and introduces a new model selection approach leveraging the DR property.

Findings

The new estimator performs well even with poor propensity score overlap.

It provides consistent estimates of treatment effects in different subpopulations.

The proposed model selection method outperforms cross-validation in simulations.

Abstract

We consider the problem of estimating the effects of a binary treatment on a continuous outcome of interest from observational data in the absence of confounding by unmeasured factors. We provide a new estimator of the population average treatment effect (ATE) based on the difference of novel double-robust (DR) estimators of the treatment-specific outcome means. We compare our new estimator with previously estimators both theoretically and via simulation. DR-difference estimators may have poor finite sample behavior when the estimated propensity scores in the treated and untreated do not overlap. We therefore propose an alternative approach, which can be used even in this unfavorable setting, based on locally efficient double-robust estimation of a semiparametric regression model for the modification on an additive scale of the magnitude of the treatment effect by the baseline covariates $X$. In contrast with existing methods, our approach simultaneously provides estimates of: i) the average treatment effect in the total study population, ii) the average treatment effect in the random subset of the population with overlapping estimated propensity scores, and iii) the treatment effect at each level of the baseline covariates $X$. When the covariate vector $X$ is high dimensional, one cannot be certain, owing to lack of power, that given models for the propensity score and for the regression of the outcome on treatment and $X$ used in constructing our DR estimators are nearly correct, even if they pass standard goodness of fit tests. Therefore to select among candidate models, we propose a novel approach to model selection that leverages the DR-nature of our treatment effect estimator and that outperforms cross-validation in a small simulation study.