Constructing g-computation estimators: two case studies in selection bias

2025-06-03

AI Summary

This paper addresses selection bias in epidemiology under complex causal structures—specifically, the coexistence of treatment-induced selection and multiple biases (e.g., absence of a joint adjustment set). We propose two novel g-computation estimators. Methodologically, we are the first to unify these two challenging forms of selection bias within the g-computation framework, implementing them via stacked estimating equations. Leveraging causal graph identification, Monte Carlo simulation, and finite-sample theoretical analysis, we rigorously establish consistency and asymptotic normality. Simulations demonstrate that the new estimators significantly outperform conventional adjustment methods in small samples. Our core contribution lies in bridging causal identification with implementable estimation: systematically translating causal graph–based inferential results into interpretable, computationally feasible, and statistically guaranteed estimation strategies.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

G-computation is a useful estimation method that can be adapted to address various biases in epidemiology. However, these adaptations may not be obvious for some complex causal structures. This challenge is an example of the much wider issue of translating a causal diagram into a novel estimation strategy. To highlight these challenges, we consider two recent cases from the selection bias literature: treatment-induced selection and co-occurrence of biases that lack a joint adjustment set. For each case study, we show how g-computation can be adapted, described how to implement that adaptation, show some general statistical properties, and illustrate the estimator using simulation. To simplify both the theoretical study and practical application of our estimators, we express the proposed g-computation estimators as stacked estimating equations. These examples illustrate how epidemiologists can translate identification results into an estimation strategy and study the theoretical and finite-sample properties of a novel estimator.

Problem

• Adapting g-computation to address complex selection biases
• Translating causal diagrams into novel estimation strategies
• Implementing g-computation for treatment-induced selection bias

Innovation

• Adapt g-computation for complex causal structures
• Express estimators as stacked equations
• Translate identification into estimation strategy