plot_uplift: bootstrap sampled uplift plots for continuous outcomes
In uplifteval: Uplift Model Evaluation with Plots and Metrics

Setup

library(grf)
library(ggplot2)
library(uplifteval)

# Utility function round logistic function maps R -> {0,1}
rl <- function(x){
  round(1/(1+exp(-x)))
}


# Generate feature data
set.seed(123)
n = 2000; p = 10
X = matrix(rnorm(n*p), n, p)
X.test = matrix(rnorm(n*p), n, p)

Examples with outcomes in {0,1}

library(grf)
library(ggplot2)
library(uplifteval)

#
# Case 1: randomized control trial, treatment propensity is feature independent and equal
# for treatment and control cases, 50-50
#
# Treatment/Response Train/Test
set.seed(123)
W = rbinom(n, 1, 0.5)
W.test = rbinom(n, 1, 0.5)
Y = rl(rl(X[,1]) * W - rl(X[,3]) * W + rnorm(n))
Y.test = rl(rl(X.test[,1]) * W.test - rl(X.test[,3]) * W.test + rnorm(n))

tau.forest = causal_forest(X, Y, W)
tau.hat = predict(tau.forest, X.test)

plot_uplift(tau.hat$predictions, W.test, Y.test)

#
# Case 2: randomized control trial, treatment propensity is feature independent but unequal
# for treatment and control cases, 80-20
#
# Treatment/Response Train/Test
set.seed(123)
W = rbinom(n, 1, 0.8)
W.test = rbinom(n, 1, 0.8)
Y = rl(rl(X[,1]) * W - rl(X[,3]) * W + rnorm(n))
Y.test = rl(rl(X.test[,1]) * W.test - rl(X.test[,3]) * W.test + rnorm(n))

table(W.test, Y.test)
rowSums(table(W.test, Y.test))

tau.forest = causal_forest(X, Y, W)
tau.hat = predict(tau.forest, X.test)


plot_uplift(tau.hat$predictions, W.test, Y.test)

Examples with continuous outcomes

# Treatment/Response Train/Test
set.seed(123)
W = rbinom(n, 1, 0.8)
W.test = rbinom(n, 1, 0.8)
Y = X[,1] * W - X[,3] * W + rnorm(n)
Y.test = X.test[,1] * W.test - X.test[,3] * W.test + rnorm(n)

tau.forest = causal_forest(X, Y, W)
tau.hat = predict(tau.forest, X.test)


plot_uplift(tau.hat$predictions, W.test, Y.test, n_bs = 10)