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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.


The 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