causalTransportR implements a number of estimators to generalize and transport causal effects by reweighting doubly-robust score functions with transformations of selection scores. All nuisance functions are cross-fit using fast supervised learning algorithms.
ateGT function implements the following estimators by aggregating estimates of individual marginal means over different marginal X distributions for the generalization and transportation case. μ, π, ρ are nuisance parameters fit using ML.
# install.packages("remotes") # if remotes isn't installed remotes::install_github("Netflix-Skunkworks/causalTransportR")