Evaluating Individualized Treatment Rules
Estimation of the Area Under Prescription Evaluation Curve (AUPEC) in ...
Estimation of the Area Under Prescription Evaluation Curve (AUPEC) in ...
Compute Quantities of Interest (PAPE, PAPEp, PAPDp, AUPEC, GATE, GATEc...
Compute Quantities of Interest (PAPE, PAPEp, PAPDp, AUPEC, GATE, GATEc...
The Consistency Test for Grouped Average Treatment Effects (GATEs) in ...
The Consistency Test for Grouped Average Treatment Effects (GATEs) und...
Create general arguments
Create arguments for bartMachine
Create arguments for bartCause
Create arguments for causal forest
Create arguments for LASSO
Create arguments for super learner
Create arguments for SVM
Create arguments for SVM classification
Create arguments for ML algorithms
Estimate individual treatment rules (ITR)
Evaluate ITR
Estimate ITR for Single Outcome
Estimation of the Grouped Average Treatment Effects (GATEs) in Randomi...
Estimation of the Grouped Average Treatment Effects (GATEs) in Randomi...
The Heterogeneity Test for Grouped Average Treatment Effects (GATEs) i...
The Heterogeneity Test for Grouped Average Treatment Effects (GATEs) u...
Estimation of the Population Average Prescription Difference in Random...
Estimation of the Population Average Prescription Difference in Random...
Estimation of the Population Average Prescription Effect in Randomized...
Estimation of the Population Average Prescription Effect in Randomized...
Estimation of the Population Average Value in Randomized Experiments
Estimation of the Population Average Value in Randomized Experiments U...
Plot the AUPEC curve
Plot the GATE estimate
Summarize estimate_itr output
Summarize test_itr output
Conduct hypothesis tests
Provides various statistical methods for evaluating Individualized Treatment Rules under randomized data. The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2019) <arXiv:1905.05389>.
Useful links