rjaf (version 0.1.2)

residualize: Arbitrary residualization of outcomes

Description

This function employs random forests and cross-validation to residualize outcomes following Section 3.3 of Ladhania et al. (2023). That is, predicted outcomes resulting from random forests are subtracted from the original outcomes. Doing so helps in adjusting for small imbalanaces in baseline covariates and removing part of the variation in outcomes common across treatment arms

Usage

residualize(data, y, vars, nfold = 5, fun.rf = "ranger")

Value

data for training and estimation with residualized outcomes.

Arguments

data

input data used for training and estimation, where each row corresponds to an individual and columns contain information on treatments, covariates, probabilities of treatment assignment, and observed outcomes.

y

a character string denoting the column name of outcomes.

vars

a vector of character strings denoting the column names of covariates.

nfold

number of folds in cross-validation. The default value is 5.

fun.rf

a character string specifying which random forest package to use. Two options are ranger and randomForest, with the default being ranger.

References

Ladhania Rahul, Spiess Jann, Ungar Lyle, Wu Wenbo (2023). Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests. https://doi.org/10.48550/arXiv.2311.00577.

Examples

data(Example_data)
library(dplyr)
library(magrittr)
Example_trainest <- Example_data %>% slice_sample(n = floor(0.5 * nrow(Example_data)))
y <- "Y"
vars <- paste0("X", 1:3)
Example_resid <- residualize(Example_trainest, y, vars, nfold = 5, fun.rf = "ranger")