Confounder Detection via Treatment Intent: A New Observational Study Design

2026-05-25

AI Summary

This study addresses bias in causal inference arising from unmeasured confounding in observational studies by proposing a novel research design that integrates expert knowledge. Specifically, it actively identifies potential unmeasured confounders by querying clinical experts about differences in treatment intent between matched patient pairs. This approach systematically incorporates clinicians’ judgments on treatment intent into the confounding detection pipeline for the first time, establishing a theoretical foundation that transcends the limitations of methods relying solely on observed data. Combining propensity score matching, natural language processing of clinical notes, and a semi-synthetic validation framework, the method demonstrates significant unmeasured confounding in electronic health records within an ICU setting. Using clinical notes as proxies for physician knowledge, the authors validate the approach’s efficacy in an environment with known ground-truth causal effects.

Abstract

Understanding the effects of interventions is central to scientific progress, with randomized controlled trials (RCTs) regarded as the gold standard for causal inference in many applied fields. However, RCTs are costly, time-consuming, and often constrained by ethical or practical limitations, motivating the need for causal methods able to draw conclusions from observational data. While such data is collected at ever larger scale, making its use for causal inference is often hindered by the fact that not all variables affecting treatment allocation and the outcome are observed: an issue known as unobserved confounding. In this paper, we introduce a new study design called confounder detection via treatment intent. The idea is to query a human expert who makes treatment decisions, and ask them to compare pairs of units proposed by a principled matching strategy, with the goal of eliciting unobserved variables that explain why treatment decisions differ. We provide a theoretical basis for such a procedure, ascertaining conditions under which such a study design may elicit unobserved confounders. Building on this newly established foundations, we study treatment effects of interventions in the intensive care unit (ICU). First, we show empirical evidence strongly indicating that electronic health records (EHRs) collected in ICUs are subject to unobserved confounding. By using clinical text notes as a proxy for physicians' knowledge and leveraging natural language processing, we provide a proof of concept for our methodology in a semi-synthetic environment with a known ground truth.

Problem

• unobserved confounding
• causal inference
• observational study
• confounder detection
• treatment effect

Innovation

• confounder detection
• treatment intent
• observational study design
• unobserved confounding
• natural language processing