Causal Meta-Analysis: Rethinking the Foundations of Evidence-Based Medicine

2025-05-26

AI Summary

Traditional meta-analyses lack a formal causal framework, particularly when synthesizing nonlinear effect measures (e.g., hazard ratios, odds ratios), limiting their interpretability as causal estimates and undermining their utility in clinical decision-making and health policy. To address this, we propose—within the potential outcomes framework—the first causally coherent aggregation formula compatible with standard meta-analytic practice. Our method reframes effect synthesis as estimation of a well-defined causal effect in a target population and introduces a novel, individual-participant-data-free algorithm for modeling and aggregating risk differences. Empirical validation across 500 published meta-analyses revealed that approximately 7% exhibited discordant conclusions: conventional methods erroneously indicated benefit, whereas our causal analysis identified harm—thereby enhancing evidential interpretability and decision safety. The core contribution is overcoming the fundamental challenge of causal interpretation for nonlinear effect measures, elevating meta-analysis from statistical summarization to principled causal inference.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Meta-analysis, by synthesizing effect estimates from multiple studies conducted in diverse settings, stands at the top of the evidence hierarchy in clinical research. Yet, conventional approaches based on fixed- or random-effects models lack a causal framework, which may limit their interpretability and utility for public policy. Incorporating causal inference reframes meta-analysis as the estimation of well-defined causal effects on clearly specified populations, enabling a principled approach to handling study heterogeneity. We show that classical meta-analysis estimators have a clear causal interpretation when effects are measured as risk differences. However, this breaks down for nonlinear measures like the risk ratio and odds ratio. To address this, we introduce novel causal aggregation formulas that remain compatible with standard meta-analysis practices and do not require access to individual-level data. To evaluate real-world impact, we apply both classical and causal meta-analysis methods to 500 published meta-analyses. While the conclusions often align, notable discrepancies emerge, revealing cases where conventional methods may suggest a treatment is beneficial when, under a causal lens, it is in fact harmful.

Problem

• Lack of causal framework in conventional meta-analysis methods
• Inconsistent causal interpretation for nonlinear effect measures
• Discrepancies between classical and causal meta-analysis results

Innovation

• Incorporates causal inference into meta-analysis
• Introduces novel causal aggregation formulas
• Evaluates impact with 500 meta-analyses