Package: personalized 0.2.8

First simulate data with complicated conditional average treatment effect/benefit score | Setup | Using xgboost for estimation of ITRs | Comparing performance with linear ITRs

Introduction to personalized | Choice of $M$ function | Choice of $f$ | Variable Selection | Extension to multi-category treatments | Quick Usage Reference | Creating and Checking Propensity Score Model | Fitting Subgroup Identification Models | Evaluating Treatment Effects within Estimated Subgroups | User Guide | Overview | Creating and Checking a propensity Score Model | Observational Studies | Randomized Controlled Trials | Explanation of Major Function Arguments | x | y | trt | propensity.func | loss | method | larger.outcome.better | cutpoint | retcall | ... | Continuous Outcomes | Binary Outcomes | Count Outcomes | Time-to-event Outcomes | Efficiency Augmentation | Plotting Fitted Models | Comparing Subgroups from a Fitted Model | Validating Subgroup Identification Models | Repeated Training/Test Splitting | Bootstrap Bias Correction | Plotting Validated Models

Efficiency augmentation | Propensity score utilities | Augmentation utilities | Comparing performance with augmentation

Example with multi-category treatments | More details on propensity scores for multi-category treatments