CausalMetaR: An R package for performing causally interpretable meta-analyses

TL;DR

CausalMetaR is an R package that enables causally interpretable meta-analyses by providing robust estimators for causal effects in target populations using multi-source data and machine learning techniques.

Contribution

The paper introduces CausalMetaR, a novel R package that implements efficient, robust, and flexible methods for causal meta-analysis with multi-source data.

Findings

Provides estimators for average and subgroup treatment effects.

Supports flexible machine learning methods and cross-fitting.

Demonstrates practical application with an example.

Abstract

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.