The CausalMetaR package provides robust and efficient
methods for estimating causal effects in a target population using a
multi-source dataset. The multi-source data can be a collection of
trials, observational studies, or a combination of both, which have the
same data structure (outcome, treatment, and covariates). The target
population can be based on an internal dataset or an external dataset
where only covariate information is available. The causal estimands
available are average treatment effects and subgroup treatment
effects.
You can install the development version of CausalMetaR
from GitHub with:
# install.packages("devtools")
devtools::install_github("ly129/CausalMetaR")