Copula-Based Endogeneity Correction for Doubly Robust Estimation of Treatment Effect
Doubly Robust (DR) estimation of treatment effect relies on an untestable assumption that is the absence of unobserved confounding. This assumption is par- ticularly problematic in the context of healthcare research, where variables like pre- scription refill rates serve as proxies for unobserved behaviors such as medication adherence. These proxy variables are often endogenous, exhibiting correlation with the regression error term due to unmeasured confounding or measurement error. We propose a copula-corrected doubly robust estimator that addresses endogeneity in both the treatment and outcome models without requiring instrumental variables. Gaussian copulas model the joint distribution...
| Authors | Sahil Shikalgar, Md. Noor-E-Alam |
|---|---|
| Primary category | stat.ME |
| Published | 5 May 2026 |
| Last updated at source | 6 May 2026 |
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