Navigation Menu

Skip to content

susanathey/causalTree

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

causalTree Introduction

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.

References

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

About

Working repository for Causal Tree and extensions

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published