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cfperformance

Overview

cfperformance provides methods for estimating model performance measures (MSE, AUC, calibration) under hypothetical/counterfactual interventions. These methods are essential when:

  • A prediction model will be deployed in settings where treatment policies differ from the training setting
  • Predictions are meant to support decisions about treatment initiation
  • You want to assess model performance after transporting from a source (e.g., RCT) to a target population

Based on Boyer, Dahabreh & Steingrimsson (2025). “Estimating and evaluating counterfactual prediction models.” Statistics in Medicine, 44(23-24), e70287. doi:10.1002/sim.70287

Installation 

# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("boyercb/cfperformance")

Quick Start 

library(cfperformance)

# Load example data
data(cvd_sim)

# Estimate counterfactual MSE under no treatment
result <- cf_mse(
  predictions = cvd_sim$risk_score,
  outcomes = cvd_sim$event,
  treatment = cvd_sim$treatment,
  covariates = cvd_sim[, c("age", "bp", "chol")],
  treatment_level = 0,     # Evaluate under no treatment
  estimator = "dr"         # Doubly robust estimator
)

result

Key Features

  • MSE/Brier Score: Loss-based performance under counterfactual intervention
  • AUC: Discrimination ability under counterfactual intervention
  • Calibration: Reliability of risk predictions under counterfactual intervention
  • Multiple Estimators: Naive, Conditional Loss/Outcome Model, IPW, and Doubly Robust
  • Inference: Bootstrap and influence function-based standard errors
  • Cross-validation: Counterfactual-aware model selection with cf_cv() and cf_compare()
  • Transportability: Evaluate model performance when transporting from a source population (e.g., RCT) to a target population

Counterfactual Performance Estimation

Estimate how a prediction model would perform under a hypothetical treatment policy (e.g., if everyone received or avoided treatment):

library(cfperformance)
data(cvd_sim)

# Compare estimators for counterfactual MSE
estimators <- c("naive", "cl", "ipw", "dr")
sapply(estimators, function(est) {
  cf_mse(
    predictions = cvd_sim$risk_score,
    outcomes = cvd_sim$event,
    treatment = cvd_sim$treatment,
    covariates = cvd_sim[, c("age", "bp", "chol")],
    treatment_level = 0,
    estimator = est
  )$estimate
})

# Estimate counterfactual AUC
cf_auc(
  predictions = cvd_sim$risk_score,
  outcomes = cvd_sim$event,
  treatment = cvd_sim$treatment,
  covariates = cvd_sim[, c("age", "bp", "chol")],
  treatment_level = 0,
  estimator = "dr"
)

# Cross-validation for model selection
cf_compare(
  models = list(
    "Simple" = event ~ age,
    "Full" = event ~ age + bp + chol
  ),
  data = cvd_sim,
  treatment = "treatment",
  treatment_level = 0,
  metric = "mse",
  K = 5
)

Transportability Analysis

The package also implements transportability estimators from Steingrimsson et al. (2022) and Voter et al. (2025) for evaluating prediction model performance when transporting from a source population (typically an RCT) to a target population:

# Load transportability example data
data(transport_sim)

# Estimate MSE in target population
tr_mse(
  predictions = transport_sim$risk_score,
  outcomes = transport_sim$event,
  treatment = transport_sim$treatment,
  source = transport_sim$source,  # 1=source/RCT, 0=target

  covariates = transport_sim[, c("age", "biomarker", "smoking")],
  treatment_level = 0,
  analysis = "transport",
  estimator = "dr"
)

# Estimate AUC in the target population
tr_auc(
  predictions = transport_sim$risk_score,
  outcomes = transport_sim$event,
  treatment = transport_sim$treatment,
  source = transport_sim$source,
  covariates = transport_sim[, c("age", "biomarker", "smoking")],
  treatment_level = 0,
  analysis = "transport",
  estimator = "dr"
)

# Estimate calibration in the target population
tr_calibration(
  predictions = transport_sim$risk_score,
  outcomes = transport_sim$event,
  treatment = transport_sim$treatment,
  source = transport_sim$source,
  covariates = transport_sim[, c("age", "biomarker", "smoking")],
  treatment_level = 0,
  analysis = "transport",
  estimator = "ipw"
)

See vignette("transportability", package = "cfperformance") for details.

Machine Learning Integration

The package supports flexible ML methods for nuisance model estimation with automatic cross-fitting for valid inference:

# Use random forest for propensity scores and outcome models
cf_mse(
  predictions = cvd_sim$risk_score,
  outcomes = cvd_sim$event,
  treatment = cvd_sim$treatment,
  covariates = cvd_sim[, c("age", "bp", "chol")],
  treatment_level = 0,
  estimator = "dr",
  propensity_model = ml_learner("ranger", num.trees = 500),
  outcome_model = ml_learner("xgboost", nrounds = 100),
  cross_fit = TRUE
)

Supported learners: - ranger - Fast random forests - xgboost - Gradient boosting - grf - Generalized random forests (honest estimation) - glmnet - Elastic net with cross-validated λ - superlearner - Ensemble learning - custom - User-supplied fit/predict functions

See vignette("ml-integration", package = "cfperformance") for details.

Documentation

See vignette("introduction", package = "cfperformance") for a comprehensive introduction.

Citation

If you use this package in your research, please cite:

Boyer CB, Dahabreh IJ, Steingrimsson JA. Estimating and evaluating counterfactual prediction models. Statistics in Medicine. 2025; 44(23-24):e70287. doi: 10.1002/sim.70287

For transportability methods, also cite:

Steingrimsson JA, Gatsonis C, Li B, Dahabreh IJ. Transporting a Prediction Model for Use in a New Target Population. American Journal of Epidemiology. 2022; 192(2):296-304. doi: 10.1093/aje/kwac128

Voter SR, et al. Counterfactual prediction from machine learning models: transportability and joint analysis for model development and evaluation using multi-source data. Diagnostic and Prognostic Research. 2025; 9(4). doi: 10.1186/s41512-025-00201-y

@article{boyer2025estimating,
  title={Estimating and Evaluating Counterfactual Prediction Models},
  author={Boyer, Christopher B. and Dahabreh, Issa J. and Steingrimsson, Jon A.},
  journal={Statistics in Medicine},
  volume={44},
  number={23-24},
  pages={e70287},
  year={2025},
  doi={10.1002/sim.70287}
}

@article{10.1093/aje/kwac128,
    title = {Transporting a Prediction Model for Use in a New Target Population},
    author = {Steingrimsson, Jon A. and Gatsonis, Constantine and Li, Bing and Dahabreh, Issa J.},
    journal = {American Journal of Epidemiology},
    volume = {192},
    number = {2},
    pages = {296-304},
    year = {2022},
    doi = {10.1093/aje/kwac128}
}

@article{voter2025transportability,
  title={Counterfactual prediction from machine learning models: transportability and joint analysis for model development and evaluation using multi-source data},
  author = {Voter, Sarah C. and Dahabreh, Issa J. and Boyer, Christopher B. and Rahbar, Habib and Kontos, Despina and Steingrimsson, Jon A.},
  journal={Diagnostic and Prognostic Research},
  volume={9},
  number={4},
  year={2025},
  doi={10.1186/s41512-025-00201-y}
}

License

MIT License