Causal Inference using GBM, Random Forests, Neural Network, and B-spline Additive Model
An R package producing robust estimation of treatment effects by fitting a collection of treatment and response models using the machine learning algorithms and regression model.
The main way to install the package is by using CRAN’s distribution. It can be installed from within R using typical install.packages() mechanism.
You can install the released version of RCATE from CRAN with:
install.packages("RCATE")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("rhli-Hannah/RCATE")This is a basic example which shows you how to solve a common problem:
library(RCATE)
## basic example
n <- 1000; p <- 3; set.seed(2223)
X <- as.data.frame(matrix(runif(n*p,-3,3),nrow=n,ncol=p))
tau = 6*sin(2*X[,1])+3*(X[,2]+3)*X[,3]
p = 1/(1+exp(-X[,1]+X[,2]))
d = rbinom(n,1,p)
t = 2*d-1
y = 100+4*X[,1]+X[,2]-3*X[,3]+tau*t/2 + rnorm(n,0,1); set.seed(2223)
x_val = as.data.frame(matrix(rnorm(200*3,0,1),nrow=200,ncol=3))
tau_val = 6*sin(2*x_val[,1])+3*(x_val[,2]+3)*x_val[,3]
# Use L1 R-learning method and GBM to estimate CATE
fit <- rcate.ml(X,y,d,method='RL',algorithm='GBM')
y_pred <- predict(fit,x_val)$predict
plot(tau_val,y_pred);abline(0,1)
# Use L1 doubly robust method and neural network to estimate CATE
fit <- rcate.ml(X,y,d,method='DR',algorithm='NN',dropout.nn=c(0,0),n.cells.nn=c(3,3))
y_pred <- predict(fit,x_val)$predict
plot(tau_val,y_pred);abline(0,1)
# Use L1 doubly robust method and random forests to estimate CATE
fit <- rcate.rf(X,y,d,method='DR',feature.frac = 0.8, minnodes = 5)
y_pred <- predict(fit,x_val)$pred
plot(tau_val,y_pred);abline(0,1)
# Use L1 R-learning and additive model to estimate CATE
n <- 1000; p <- 2; set.seed(2223)
X <- as.data.frame(matrix(runif(n*p,-3,3),nrow=n,ncol=p))
tau = 3*X[,1]-2*X[,2]
p = 1/(1+exp(-X[,1]+X[,2]))
d = rbinom(n,1,p)
t = 2*d-1
y = 100+tau*t/2 + rnorm(n,0,1); set.seed(2223)
x_val = as.data.frame(matrix(rnorm(200*2,0,1),nrow=200,ncol=2))
tau_val = 3*x_val[,1]-2*x_val[,2]
fit <- rcate.am(X,y,d,lambda.smooth = 2, method = 'RL')
y_pred <- predict(fit,x_val)$pred
plot(tau_val,y_pred);abline(0,1)
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