Copula-Based Endogeneity Correction for Doubly Robust Estimation of Treatment Effect
Sahil Shikalgar, Md. Noor-E-Alam

TL;DR
This paper introduces a copula-based correction method for doubly robust treatment effect estimation that addresses endogeneity without needing instrumental variables, demonstrated through simulations and real data.
Contribution
It develops a novel copula-corrected doubly robust estimator that handles endogenous covariates in treatment effect models without requiring instruments.
Findings
Naive DR estimation is biased under endogeneity.
The copula correction recovers unbiased treatment effects.
Application shows the correction changes the significance of treatment effects.
Abstract
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 of endogenous covariates and the error term, enabling consistent estimation while preserving the doubly robust property that requires correct specification of either the treatment or outcome model, not…
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