The causalTree function builds a regression model and returns an rpart object, which is the object derived from rpart package, implementing many ideas in the CART (Classification and Regression Trees), written by Breiman, Friedman, Olshen and Stone. Like rpart, causalTree builds a binary regression tree model in two stages, but focuses on estimating heterogeneous causal effect.

To install this package in R, run the following commands:

install.packages("devtools")
library(devtools) 
install_github("susanathey/causalTree")

Example usage:

library(causalTree)
tree <- causalTree(y~ x1 + x2 + x3 + x4, data = simulation.1, treatment = simulation.1$treatment,
                   split.Rule = "CT", cv.option = "CT", split.Honest = T, cv.Honest = T, split.Bucket = F, 
                   xval = 5, cp = 0, minsize = 20, propensity = 0.5)
                  
opcp <- tree$cptable[,1][which.min(tree$cptable[,4])]

opfit <- prune(tree, opcp)

rpart.plot(opfit)

For More details, please check out briefintro.pdf.

Susan Athey, Guido Imbens. Recursive Partitioning for Heterogeneous Causal Effects. [link]