An R package for conformal inference of counterfactuals and individual treatment effects
This R package implements weighted conformal inference-based procedures for counterfactuals and individual treatment effects proposed in our paper: Conformal Inference of Counterfactuals and Individual Treatment Effects. It includes both the split conformal inference and cross-validation+. For each type of conformal inference, both conformalized quantile regression (CQR) and standard conformal inference are supported. It provides a pool of convenient learners and allows flexible user-defined learners for conditional mean and quantiles.
conformalCf()produces intervals for counterfactuals or outcomes with missing values in general.conformalIte()produces intervals for individual treatment effects with a binary treatment under the potential outcome framework.conformal()provides a generic framework of weighted conformal inference for continuous outcomes.conformalInt()provides a generic framework of weighted conformal inference for interval outcomes.
if (!require("devtools")){
install.packages("devtools")
}
devtools::install_github("lihualei71/cfcausal")
To access the vignette, run the following code to build it.
devtools::install_github("lihualei71/cfcausal", build_vignettes = TRUE)
We suggest installing grf, randomForest, gbm and bartMachine to take advantage of the built-in learners.
We illustrate the usage of cfcausal package using simple synthetic datasets. For details please read the vignette (vignette("cfcausal_demo", package = "cfcausal")) and the manual.
#### Conformal inference of counterfactuals
library("cfcausal")
# Generate data
set.seed(1)
n <- 1000
d <- 5
X <- matrix(rnorm(n * d), nrow = n)
beta <- rep(1, 5)
Y <- X %*% beta + rnorm(n)
# Generate missing indicators
missing_prob <- pnorm(X[, 1])
if_missing <- missing_prob < runif(n)
Y[if_missing] <- NA
# Generate testing data
ntest <- 5
Xtest <- matrix(rnorm(ntest * d), nrow = ntest)
# Run weighted split CQR
obj <- conformalCf(X, Y, type = "CQR",
quantiles = c(0.05, 0.95),
outfun = "quantRF", useCV = FALSE)
predict(obj, Xtest, alpha = 0.1)
# Run weighted CQR-CV+
obj <- conformalCf(X, Y, type = "CQR",
quantiles = c(0.05, 0.95),
outfun = "quantRF", useCV = TRUE)
predict(obj, Xtest, alpha = 0.1)
#### Conformal inference of individual treatment effects
library("cfcausal")
# Generate potential outcomes from two linear models
set.seed(1)
n <- 1000
d <- 5
X <- matrix(rnorm(n * d), nrow = n)
beta <- rep(1, 5)
Y1 <- X %*% beta + rnorm(n)
Y0 <- rnorm(n)
# Generate treatment indicators
ps <- pnorm(X[, 1])
T <- as.numeric(ps < runif(n))
Y <- ifelse(T == 1, Y1, Y0)
# Generate testing data
ntest <- 5
Xtest <- matrix(rnorm(ntest * d), nrow = ntest)
# Inexact nested method
CIfun <- conformalIte(X, Y, T, alpha = 0.1,
algo = "nest", exact = FALSE, type = "CQR",
quantiles = c(0.05, 0.95),
outfun = "quantRF", useCV = FALSE)
CIfun(Xtest)
# Exact nested method
CIfun <- conformalIte(X, Y, T, alpha = 0.1,
algo = "nest", exact = TRUE, type = "CQR",
quantiles = c(0.05, 0.95),
outfun = "quantRF", useCV = FALSE)
CIfun(Xtest)
# Naive method
CIfun <- conformalIte(X, Y, T, alpha = 0.1,
algo = "naive", type = "CQR",
quantiles = c(0.05, 0.95),
outfun = "quantRF", useCV = FALSE)
CIfun(Xtest)
# Counterfactual method, Y and T needs to be observed
pstest <- pnorm(Xtest[, 1])
Ttest <- as.numeric(pstest < runif(ntest))
Y1test <- Xtest %*% beta + rnorm(ntest)
Y0test <- rnorm(ntest)
Ytest <- ifelse(Ttest == 1, Y1test, Y0test)
CIfun <- conformalIte(X, Y, T, alpha = 0.1,
algo = "counterfactual", type = "CQR",
quantiles = c(0.05, 0.95),
outfun = "quantRF", useCV = FALSE)
CIfun(Xtest, Ytest, Ttest)