Estimate Causal Effects with Borrowing Between Data Sources

Estimate population average treatment effects from a primary data source with borrowing from supplemental sources. Causal estimation is done with either a Bayesian linear model or with Bayesian additive regression trees (BART) to adjust for confounding. Borrowing is done with multisource exchangeability models (MEMs). For information on BART, see Chipman, George, & McCulloch (2010) . For information on MEMs, see Kaizer, Koopmeiners, & Hobbs (2018) .

borrowr

R package for estimating the population average treatment effect using a primary data source with borrowing from supplemental data sources.

To install from source and build vignettes:

devtools::install_github("jeffrey-boatman/borrowr", build = TRUE, build_opts = c("--no-resave-data", "--no-manual"), force = TRUE)

To do:

• update documentation (changes included: adding argument for prior probability of exchangeability, updated gamma prior for bayesian linear model, ...)