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.

翻译：元分析通过综合来自不同环境中多项研究的效应估计值，位居临床研究证据层级的顶端。然而，基于固定效应或随机效应模型的传统方法缺乏因果框架，这可能限制其在公共政策中的可解释性和实用性。引入因果推断将元分析重新定义为对明确定义人群的清晰因果效应的估计，从而能够以规范的方式处理研究异质性。我们表明，当效应以风险差异衡量时，经典元分析估计量具有清晰的因果解释。然而，对于风险比和比值比等非线性测量指标，这种解释不再成立。为解决这一问题，我们提出了新颖的因果聚合公式，这些公式与标准元分析实践兼容，且无需访问个体层面数据。为评估实际影响，我们将经典与因果元分析方法应用于500项已发表的元分析。虽然结论通常一致，但出现了显著差异，揭示出在某些情况下，传统方法可能表明治疗有益，而从因果视角看，该治疗实际上是有害的。