Researchers would often like to leverage data from a collection of sources (e.g., primary studies in a meta-analysis) to estimate causal effects in a target population of interest. However, traditional meta-analytic methods do not produce causally interpretable estimates for a well-defined target population. In this paper, we present the CausalMetaR R package, which implements efficient and robust methods to estimate causal effects in a given internal or external target population using multi-source data. The package includes estimators of average and subgroup treatment effects for the entire target population. To produce efficient and robust estimates of causal effects, the package implements doubly robust and non-parametric efficient estimators and supports using flexible data-adaptive (e.g., machine learning techniques) methods and cross-fitting techniques to estimate the nuisance models (e.g., the treatment model, the outcome model). We describe the key features of the package and demonstrate how to use the package through an example.

翻译：研究者通常希望利用多源数据（例如荟萃分析中的原始研究）来估计目标人群的因果效应。然而，传统荟萃分析方法无法为明确定义的目标人群提供因果可解释的估计。本文介绍了CausalMetaR R包，该包实现了高效且稳健的方法，可利用多源数据估计内部或外部目标人群的因果效应。该包包含针对整个目标人群的平均处理效应和亚组处理效应的估计量。为生成高效稳健的因果效应估计，该包实现了双重稳健和非参数有效估计量，并支持使用灵活的数据自适应方法（例如机器学习技术）和交叉拟合技术来估计干扰模型（例如治疗模型、结局模型）。我们描述了该包的核心功能，并通过一个示例演示了其使用方法。