plUpliftEval: A helper for the new_PlUpliftEval function that validates the...

In uplifteval: Uplift Model Evaluation with Plots and Metrics

Description

A helper for the new_PlUpliftEval function that validates the treatment, outcome, prediction, p, and n_bins arguments.

Usage

plUpliftEval(treatment, outcome, prediction, p = "infer", n_bins = 20)

Arguments

treatment	numeric vector of treatment identifiers
outcome	numeric vector of outcomes
prediction	numeric vector of uplift predictions
p	optional "infer", numeric, numeric vector representing treatment propensities
n_bins	integer number of bins on x-axis; default 20

Value

a list representing a pylift uplift eval object

Examples

set.seed(0)
rl <- function(x){
  round(1/(1+exp(-x)))
}
n <- 2000; p <- 3
beta <- -0.5
X <- matrix(rnorm(n*p), n, p)
W <- rbinom(n, 1, 0.5)
Y <- rl(pmax(beta+X[,1], 0) * W + X[,2])
p1 <- 1/(1+exp(-(beta+X[,1])))
plUpliftEval(W, Y, p1)


library(grf)
set.seed(123)

rl <- function(x){
  round(1/(1+exp(-x)))
}
n <- 2000; p <- 10
X <- matrix(rnorm(n*p), n, p)
W <- rbinom(n, 1, 0.2)
Y <- rl(rl(X[,1]) * W - rl(X[,3]) * W + rnorm(n))
tau.forest <- causal_forest(X, Y, W)
tau.hat <- predict(tau.forest, X)
plue <- plUpliftEval(W, Y, tau.hat$predictions)
plue

uplifteval documentation built on June 15, 2019, 9:03 a.m.