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evalITR

R package evalITR provides various statistical methods for estimating and evaluating Individualized Treatment Rules under randomized data. The provided metrics include (1) population average prescriptive effect PAPE; (2) population average prescriptive effect with a budget constraint PAPEp; (3) population average prescriptive effect difference with a budget constraint PAPDp; (4) and area under the prescriptive effect curve AUPEC; (5) Grouped Average Treatment Effects GATEs. The details of the methods for this design are given in Imai and Li (2023) and Imai and Li.

Documentation and website: https://michaellli.github.io/evalITR/

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Install

install.packages('evalITR')

Monthly Downloads

263

Version

1.0.0

License

GPL (>= 2)

Issues

3

Pull Requests

4

Stars

14

Forks

4

Maintainer

Michael Lingzhi Li

Last Published

August 25th, 2023

Functions in evalITR (1.0.0)

create_ml_args_superLearner

Create arguments for super learner
plot_estimate

Plot the GATE estimate
fit_itr

Estimate ITR for Single Outcome
summary.test_itr

Summarize test_itr output
test_itr

Conduct hypothesis tests
create_ml_args_svm_cls

Create arguments for SVM classification
compute_qoi_user

Compute Quantities of Interest (PAPE, PAPEp, PAPDp, AUPEC, GATE, GATEcv) with user defined functions
hetcv.test

The Heterogeneity Test for Grouped Average Treatment Effects (GATEs) under Cross Validation in Randomized Experiments
compute_qoi

Compute Quantities of Interest (PAPE, PAPEp, PAPDp, AUPEC, GATE, GATEcv)
het.test

The Heterogeneity Test for Grouped Average Treatment Effects (GATEs) in Randomized Experiments
estimate_itr

Estimate individual treatment rules (ITR)
create_ml_arguments

Create arguments for ML algorithms
print.summary.itr

Print
print.summary.test_itr

Print
consist.test

The Consistency Test for Grouped Average Treatment Effects (GATEs) in Randomized Experiments
consistcv.test

The Consistency Test for Grouped Average Treatment Effects (GATEs) under Cross Validation in Randomized Experiments
summary.itr

Summarize estimate_itr output
star

Tennessee’s Student/Teacher Achievement Ratio (STAR) project
GATE

Estimation of the Grouped Average Treatment Effects (GATEs) in Randomized Experiments
create_ml_args

Create general arguments
create_ml_args_causalforest

Create arguments for causal forest
create_ml_args_bart

Create arguments for bartMachine
create_ml_args_bartc

Create arguments for bartCause
create_ml_args_lasso

Create arguments for LASSO
PAPE

Estimation of the Population Average Prescription Effect in Randomized Experiments
PAPDcv

Estimation of the Population Average Prescription Difference in Randomized Experiments Under Cross Validation
AUPEC

Estimation of the Area Under Prescription Evaluation Curve (AUPEC) in Randomized Experiments
GATEcv

Estimation of the Grouped Average Treatment Effects (GATEs) in Randomized Experiments Under Cross Validation
PAPEcv

Estimation of the Population Average Prescription Effect in Randomized Experiments Under Cross Validation
PAV

Estimation of the Population Average Value in Randomized Experiments
PAVcv

Estimation of the Population Average Value in Randomized Experiments Under Cross Validation
PAPD

Estimation of the Population Average Prescription Difference in Randomized Experiments
AUPECcv

Estimation of the Area Under Prescription Evaluation Curve (AUPEC) in Randomized Experiments Under Cross Validation
evaluate_itr

Evaluate ITR
plot.itr

Plot the AUPEC curve
create_ml_args_svm

Create arguments for SVM