This package implements the R-learner for estimating
heterogeneous treatment effects, as proposed by Nie and Wager (2017). We consider a
setup where we observe data (X, W, Y) generated according
to the following general non-parameteric model
X ~ P(X)
W ~ P(W|X) where W is in {0,1}
Y = b(X) + (W-0.5)*tau(X) + epsilon
with E[epsilon | X, W] = 0.
The R-learner provides a general framework to estimate the heterogeneous treatment effect tau(X). We first estimate marginal effects and treatment propensities in order to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. The R-learner is flexible and easy to use: For both steps, we can use any loss-minimization method, e.g., the lasso, random forests, boosting, etc.; moreover, these methods can be fine-tuned by cross validation.
The package implements the R-learner using various machine learning models. In particular, the function rlasso is a lightweight implementation of the R-learner using the lasso (glmnet) and uses cv.glmnet for cross-fitting and cross-validation; the function rboost is a lightweight implementation of the R-learner using gradient boosting (xgboost), and by default randomly searches over a set of hyper-parameter combinations used in xgboost, while cross-validating on the number of trees with an early stopping option for each of the random searches; the function rkern is a lightweight implementation of the R-learner using kernel ridge regression with a Gaussian kernel using a version of the KRLS package. The version of the KRLS package can be found here. Note the version number we use is 1.1.1. It is adapted from the KRLS2 package version 1.1.0.
This package is currently in beta, and we expect to make continual improvements to its performance and usability.
This package is written and maintained by Xinkun Nie (xinkun@stanford.edu), Alejandro Schuler, and Stefan Wager.
To install this package in R, run the following commands:
library(devtools)
install_github("xnie/rlearner")Below is an example of using the function rlasso, rboost, and rkern.
library(rlearner)
n = 100; p = 10
x = matrix(rnorm(n*p), n, p)
w = rbinom(n, 1, 0.5)
y = pmax(x[,1], 0) * w + x[,2] + pmin(x[,3], 0) + rnorm(n)
rlasso_fit = rlasso(x, w, y)
rlasso_est = predict(rlasso_fit, x)
rboost_fit = rboost(x, w, y)
rboost_est = predict(rboost_fit, x)
rkern_fit = rkern(x, w, y)
rkern_est = predict(rkern_fit, x)The package also implements S-, T-, U-, and X-learners. These can be called in a similar fashion. For example,
tlasso_fit = tlasso(data$x, data$w, data$y)
tlasso_tau_hat = predict(tlasso_fit, data$x)
tboost_fit = tboost(data$x, data$w, data$y)
tboost_tau_hat = predict(tboost_fit, data$x)
tkern_fit = tkern(data$x, data$w, data$y)
tkern_tau_hat = predict(tkern_fit, data$x)All simulation results in Nie and Wager (2020+) can be reproduced using this package, with the experiments implemented under the directory /experiments_for_paper.
Xinkun Nie and Stefan Wager. Quasi-Oracle Estimation of Heterogeneous Treatment Effects. Biometrika, forthcoming [arxiv]