A doubly robust estimator for continuous treatments in high dimensions.

ABSTRACT:

Background

Generalized propensity score (GPS) methods have become popular for estimating causal relationships between a continuous treatment and an outcome in observational studies with rich covariate information. The presence of rich covariates enhances the plausibility of the unconfoundedness assumption. Nonetheless, it is also crucial to ensure the correct specification of both marginal and conditional treatment distributions, beyond the assumption of unconfoundedness.

Method

We address limitations in existing GPS methods by extending balance-based approaches to high dimensions and introducing the Generalized Outcome-Adaptive LASSO and Doubly Robust Estimate (GOALDeR). This novel approach integrates a balance-based method that is robust to the misspecification of distribu

SUBMITTER: Gao Q 

PROVIDER: S-EPMC11823051 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature