Time-varying confounding in epidemic intervention evaluations

2025-08-18

AI Summary

This study addresses bias in causal effect estimation arising from time-varying confounding in epidemic intervention evaluation: public health interventions are both influenced by prior epidemic states and subsequently affect disease transmission, inducing bidirectional temporal dependence that biases conventional associative models. To address this, we propose a causal inference framework integrating structural causal models with agent-based epidemic simulation, explicitly modeling and adjusting for time-varying confounders. Our approach systematically elucidates the mechanistic pathways through which temporal confounding induces bias at the population level, filling a critical gap in epidemiology regarding systematic causal analysis of time-varying confounding. Simulation studies demonstrate that neglecting such confounding substantially distorts intervention effect estimates, whereas our framework effectively corrects this bias. The method provides both theoretical grounding and a practical, implementable tool for unbiased causal assessment from observational data.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Estimating the causal effect of a time-varying public health intervention on the course of an infectious disease epidemic is an important methodological challenge. During the COVID-19 pandemic, researchers attempted to estimate the effects of social distancing policies, stay-at-home orders, school closures, mask mandates, vaccination programs, and many other interventions on population-level infection outcomes. However, measuring the effect of these interventions is complicated by time-varying confounding: public health interventions are causal consequences of prior outcomes and interventions, as well as causes of future outcomes and interventions. Researchers have shown repeatedly that neglecting time-varying confounding for individual-level longitudinal interventions can result in profoundly biased estimates of causal effects. However, the issue with time-varying confounding bias has often been overlooked in population-level epidemic intervention evaluations. In this paper, we explain why associational modeling to estimate the effects of interventions on epidemic outcomes based on observations can be prone to time-varying confounding bias. Using causal reasoning and model-based simulation, we show how directional bias due to time-varying confounding arises in associational modeling and the misleading conclusions it induces.

Problem

• Addresses time-varying confounding in epidemic interventions
• Evaluates causal effects of public health measures
• Identifies bias in associational modeling approaches

Innovation

• Causal reasoning addresses time-varying confounding
• Model-based simulation demonstrates directional bias
• Associational modeling limitations in epidemic evaluations