README.md

In jiongyi-cao/tSneClstGRF: t-SNE Clustering analysis for causal forest object

tSneClstGRF is an R package that generates an interactive Shiny interface for easily visualizing and exploring heterogeneous causal effect estimated by causal forest proposed by Athey et al. (2018).

Installation

To install this package from the github repository, use:

if(!require(devtools)) install.packages("devtools") # If not already installed
devtools::install_github("jiongyi-cao/tSneClstGRF")

Usage

A basic workflow of tSneClstGRF contains major three steps:

• read causal forest object
• run t-SNE analysis
• generate shiny interface

The following is an illustration using the replication from Athey, S., & Wager, S. (2019). Estimating treatment effects with causal forests: An application.

library(grf)
library(tSneClstGRF)
#load dataset
data(nslm)
X <- nslm[,-c(29,30,31)]
attach(nslm)
## Replication from Athey & Stefan's GRF application
Y.forest = regression_forest(X, Y, clusters = school.id)
Y.hat = predict(Y.forest)$predictions
W.forest = regression_forest(X, W, clusters = school.id)
W.hat = predict(W.forest)$predictions

cf.raw = causal_forest(X, Y, W,
                       Y.hat = Y.hat, W.hat = W.hat,
                       clusters = school.id)
varimp = variable_importance(cf.raw)
selected.idx = which(varimp > mean(varimp))

cf = causal_forest(X[,selected.idx], Y, W,
                   Y.hat = Y.hat, W.hat = W.hat,
                   clusters = school.id,
                   #samples.per.cluster = 50,
                   tune.parameters = "all")

#read causal forest object
my_cf <- read.cf(cf)
#run t-SNE Clustering algorithm
tsne_obj <- run.analsis(my_cf,3000)
#generate shiny application
create.shiny(tsne_obj$result)

References

Athey Susan, Julie Tibshirani, and Stefan Wager. Generalized Random Forests. Annals of Statistics (forthcoming), 2018

Athey, S., & Wager, S. Estimating treatment effects with causal forests: An application.,2019

jiongyi-cao/tSneClstGRF documentation built on June 2, 2022, 11:40 p.m.