https://github.com/susanathey/causalTree

Working repository for Causal Tree and extensions
https://github.com/susanathey/causalTree

Last synced: 3 days ago
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Working repository for Causal Tree and extensions

• Host: GitHub
• URL: https://github.com/susanathey/causalTree
• Owner: susanathey
• License: gpl-3.0
• Created: 2016-04-22T00:11:35.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2020-04-22T16:19:10.000Z (over 4 years ago)
• Last Synced: 2024-08-02T06:02:11.678Z (3 months ago)
• Language: C
• Size: 3.5 MB
• Stars: 429
• Watchers: 41
• Forks: 149
• Open Issues: 14
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# 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:

```R

install.packages("devtools")

library(devtools) 

install_github("susanathey/causalTree")

```

Example usage:

```R

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]