  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.
