Recent work has made important contributions in the development of causally-interpretable meta-analysis. These methods transport treatment effects estimated in a collection of randomized trials to a target population of interest. Ideally, estimates targeted toward a specific population are more interpretable and relevant to policy-makers and clinicians. However, between-study heterogeneity not arising from differences in the distribution of treatment effect modifiers can raise difficulties in synthesizing estimates across trials. The existence of such heterogeneity, including variations in treatment modality, also complicates the interpretation of transported estimates as a generic effect in the target population. We propose a conceptual framework and estimation procedures that attempt to account for such heterogeneity, and develop inferential techniques that aim to capture the accompanying excess variability in causal estimates. This framework also seeks to clarify the kind of treatment effects that are amenable to the techniques of generalizability and transportability.

翻译：近期研究在因果可解释的meta分析方法论发展方面取得了重要进展。这些方法将随机试验集合中估计的治疗效应外推到感兴趣的目标人群。理想情况下，针对特定人群的效应估计对政策制定者和临床医生而言更具解释性和相关性。然而，由治疗效应修饰因子分布差异之外的因素引起的跨研究异质性，会给各试验间效应估计的综合带来困难。此类异质性（包括治疗方式的差异）的存在，也使得外推效应估计在目标人群中作为通用效应的解释变得复杂。我们提出了一个概念性框架和估计程序，旨在解释此类异质性，并发展了旨在捕捉因果估计中伴随的额外变异性的推断技术。该框架同时致力于阐明适用于可推广性与可迁移性技术分析的处理效应类型。