CFCF

Cross-fitted Causal Forests

This package implements a causal forest (Wager and Athey, 2018) learned (cross-fitted) with training and holdout data, fit over K folds, as in Hatamyar and Kreif (2023). Forests are estimated with all K-1 folds, then estimates are predicted for the holdout (kth) fold. This is done K times with each fold used as the holdout fold once. This is repeated a specified number of times and the results are averaged across folds and repetitions. See Hatamyar and Kreif (2023) for further details.

Installation

# Install the development version from GitHub:
devtools::install_github("jhatamyar/CFCF")

Usage

library(CFCF)

# Generate data.
n <- 2000
p <- 10 

## matrix of covariates, vector of treatment and outcomes 
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.4 + 0.2 * (X[, 1] > 0))
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)


## run the function: NOTE THIS WILL TAKE A FEW MINUTES 
crossfitted_CF <- CFCF(X, Y, W)

Outputs

## CATES (Conditional Average Treatment Effects)
crossfitted_CF$cates

## ATE (Average Treatment Effect) 
crossfitted_CF$ATE

## Double-Robust Scores
crossfitted_CF$scores

The function also outputs standard errors of CATEs and ATE, estimated propensity scores, and the separate double-robust scores for treatment and control.

References

Julia Hatamyar and Noemi Kreif. Policy Learning with Rare Outcomes, 2023, arxiv

Stefan Wager and Susan Athey. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association, 113(523), 2018.