causalCalibration: Causal Isotonic Calibration for Heterogeneous Treatment Effects

Provides R code implementing causal isotonic calibration and cross-calibration as described in the manuscript "Causal Isotonic Calibration for Heterogeneous Treatment Effects" by Lars van der Laan, Ernesto Ulloa, Marco Carone, and Alex Luedtke.

A preprint describing methods and algorithms can be found at this link: https://arxiv.org/abs/2302.14011. This work was published in the International Conference on Machine Learning (ICML) (2023) and can be found here: https://proceedings.mlr.press/v202/van-der-laan23a/van-der-laan23a.pdf

See the vignette for code examples.

install.packages(“devtools”)
library(devtools)
install_github(“larsvanderlaan/causalCalibration”)
library(causalCalibration)
install_github(“tlverse/sl3”)
library(sl3)

set.seed(123)
n <- 1000
W <- runif(n, -1 , 1)
pA1 <- plogis(2*W)
A <- rbinom(n, size = 1, pA1)
CATE <- 1 + W
EY0 <- W
EY1 <- W + CATE
Y <- rnorm(n, W + A * CATE, 0.3)

# Initial cross-fitted learner of CATE
data <- data.frame(W, A, Y)
K <- 10 # 10-fold cross-fitting
task <- sl3_Task$new(data, covariates = c("W"), outcome = "Y", folds = K) # sl3_Task for T-learner
tmp <- task$folds # used to generate folds internally

# crossfit gam learner using sl3
lrnr <- Lrnr_cv$new(Lrnr_gam$new())

# Compute T-learner for CATE.
lrnr_trained_A0 <- lrnr$train(task[A==0])
lrnr_trained_A1 <- lrnr$train(task[A==1])

# get pooled out-of-fold predictions
initial_CATE_preds_pooled <- lrnr_trained_A1$predict(task) - lrnr_trained_A0$predict(task)

# train calibrator
calibrator <- causalCalibrate(tau, A, Y, EY1, EY0, pA1, tau_pred = initial_CATE_preds_pooled)


#generate new dataset to obtain calibrated predictions
n <- 200
W <- runif(n, -1 , 1)
new_data <- data.frame(W)

# new task
new_task <- sl3_Task$new(new_data, covariates = c("W"), outcome = c(), folds = 10)

# get matrix of fold-specific initial CATE predictions from new task
initial_CATE_cfpreds_mat <- do.call(cbind, lapply(1:K, function(k) {
  # get predictions using learner trained on kth training set
  tau_k <- lrnr_trained_A1$predict_fold(new_task, k) - lrnr_trained_A0$predict_fold(new_task, k)
  return(tau_k)
}))
# use cross_calibrate to collapse the predictions from the K initial cross-fitted CATE predictors into a single calibrated prediction. 
new_CATE_preds_crosscalibrated <- cross_calibrate(calibrator, initial_CATE_cfpreds_mat)
plot(W,  new_CATE_preds_crosscalibrated)

Citation

To reference this work, please use the following bibtex citation:

@inproceedings{van2023causal,
  title={Causal isotonic calibration for heterogeneous treatment effects},
  author={{van der Laan}, Lars and Ulloa-P{\'e}rez, Ernesto and Carone, Marco and Luedtke, Alex},
  booktitle={Proceedings of the 40th International Conference on Machine Learning (ICML)},
  year={2023},
  address={Honolulu, Hawaii, USA},
  publisher={PMLR},
  volume={202},
    pdf={https://proceedings.mlr.press/v202/van-der-laan23a/van-der-laan23a.pdf}
}

Larsvanderlaan/causalCalibration

causalCalibration: Causal Isotonic Calibration for Heterogeneous Treatment Effects

Citation