A roadmap for systematic identification and analysis of multiple biases in causal inference

2025-04-11

AI Summary

In observational studies, multiple unmeasured confounders often violate causal identification assumptions, inducing bias in effect estimation; existing quantitative bias analysis (QBA) methods typically address only single-source bias and cannot assess their joint impact. This paper introduces the first systematic framework for simultaneous identification and concurrent adjustment of multiple biases, integrating causal graph modeling, idealized trial simulation, Monte Carlo simulation, and counterfactual estimation to quantify the aggregate effect of multiple biases. Unlike conventional sequential or single-bias correction approaches, our method enables holistic bias adjustment. We validate it empirically in a study of breastfeeding and childhood asthma: simulations demonstrate that concurrent adjustment substantially improves the robustness, credibility, and reproducibility of causal effect estimates. The framework provides a generalizable methodological foundation for rigorous causal inference from observational data.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• 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

Abstract

Observational studies examining causal effects rely on unverifiable causal assumptions, the violation of which can induce multiple biases. Quantitative bias analysis (QBA) methods examine the sensitivity of findings to such violations, generally by producing bias-adjusted estimates under alternative assumptions. Common strategies for QBA address either a single source of bias or multiple sources one at a time, thus not informing the overall impact of the potential biases. We propose a systematic approach (roadmap) for identifying and analysing multiple biases together. Briefly, this consists of (i) articulating the assumptions underlying the primary analysis through specification and emulation of the"ideal trial"that defines the causal estimand of interest and depicting these assumptions using casual diagrams; (ii) depicting alternative assumptions under which biases arise using causal diagrams; (iii) obtaining a single estimate simultaneously adjusted for all biases under the alternative assumptions. We illustrate the roadmap in an investigation of the effect of breastfeeding on risk of childhood asthma. We further use simulations to evaluate a recent simultaneous adjustment approach and illustrate the need for simultaneous rather than one-at-a-time adjustment to examine the overall impact of biases. The proposed roadmap should facilitate the conduct of high-quality multiple bias analyses.

Problem

• Identifying and analyzing multiple biases in causal inference simultaneously
• Addressing limitations of single-bias adjustment methods in observational studies
• Providing a systematic approach for bias-adjusted causal effect estimation

Innovation

• Systematic identification of multiple biases together
• Simultaneous adjustment for all biases
• Causal diagrams for assumption articulation