Causal inference with outcome dependent sampling and mismeasured outcome

TL;DR

This paper develops a new method to accurately estimate the average treatment effect in outcome-dependent sampling studies with mismeasured outcomes, addressing a gap in existing methodologies.

Contribution

It establishes identifiability of ATE under these conditions and proposes a consistent estimator using generalized linear and additive models with penalized B-splines.

Findings

Estimator is consistent under regularity conditions

Method performs well in simulation studies

Applied successfully to UK Biobank data

Abstract

Outcome-dependent sampling designs are extensively utilized in various scientific disciplines, including epidemiology, ecology, and economics, with retrospective case-control studies being specific examples of such designs. Additionally, if the outcome used for sample selection is also mismeasured, then it is even more challenging to estimate the average treatment effect (ATE) accurately. To our knowledge, no existing method can address these two issues simultaneously. In this paper, we establish the identifiability of ATE and propose a novel method for estimating ATE in the context of generalized linear model. The estimator is shown to be consistent under some regularity conditions. To relax the model assumption, we also consider generalized additive model. We propose to estimate ATE using penalized B-splines and establish asymptotic properties for the proposed estimator. Our methods are evaluated through extensive simulation studies and the application to a dataset from the UK Biobank, with alcohol intake as the treatment and gout as the outcome.