Demystifying Proximal Causal Inference

TL;DR

Proximal causal inference (PCI) offers a framework for estimating causal effects despite unobserved confounders by using proxy variables, with guidance on assumptions, proxy selection, and practical implementation.

Contribution

This paper reviews PCI identification results, discusses assumptions, compares estimation methods, and provides practical guidance for applying PCI in real-world scenarios.

Findings

PCI can identify causal effects with proxies for unmeasured confounders.

Guidance on proxy variable selection and evaluation is provided.

Illustrates tensions and considerations in proxy choice through examples.

Abstract

Proximal causal inference (PCI) has emerged as a promising framework for identifying and estimating causal effects in the presence of unobserved confounders. While many traditional causal inference methods rely on the assumption of no unobserved confounding, this assumption is likely often violated. PCI addresses this challenge by relying on an alternative set of assumptions regarding the relationships between treatment, outcome, and auxiliary variables that serve as proxies for unmeasured confounders. We review existing identification results, discuss the assumptions necessary for valid causal effect estimation via PCI, and compare different PCI estimation methods. We offer practical guidance on operationalizing PCI, with a focus on selecting and evaluating proxy variables using domain knowledge, measurement error perspectives, and negative control analogies. Through conceptual examples, we demonstrate tensions in proxy selection and discuss the importance of clearly defining the unobserved confounding mechanism. By bridging formal results with applied considerations, this work aims to demystify PCI, encourage thoughtful use in practice, and identify open directions for methodological development and empirical research.