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

TL;DR

This paper introduces a causal inference framework for meta-analysis, addressing interpretability issues and discrepancies in nonlinear effect measures, with practical applications to existing meta-analyses.

Contribution

It develops novel causal aggregation formulas compatible with standard meta-analysis, enhancing causal interpretability without needing individual data.

Findings

Classical meta-analysis estimators have causal interpretation for risk differences.

Discrepancies found between classical and causal meta-analysis in real data.

Causal meta-analysis can reveal harmful effects overlooked by traditional methods.

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.