Package index • hte3

Production API

hte_task(): Create an hte3 Task with a Production-Oriented Interface
fit_cate(): Fit a CATE Model
fit_crr(): Fit a CRR Model
grf_cate(): Fit GRF-Backed CATE Models
grf_crr(): Fit GRF-Backed CRR Models
predict(<hte3_model>) summary(<hte3_model>): S3 Methods for Production hte3 Models
hte3_example_data(): Simulate Tiny Example Data for hte3

Advanced API

hte3_Task: Task object for meta-learners in causal data structures.
make_hte3_Task_tx(): Task object for meta-learners in the point-treatment setting.
make_cross_fitted(): Create Cross-Fitted Learner
calibrate(): Calibrate Predictor-Outcome Relationship
estimate_m(): Helper function for estimation of treatment-averaged outcome regression using sl3.
estimate_mu(): Helper function for estimation of outcome regression using sl3.
estimate_pi(): Helper function for estimation of propensity score using sl3.
make_ep_stack(): Create an Ensemble of CATE EP-learners with Varying Sieve Basis Sizes
make_grf_nuisance_learners(): Build Default GRF Nuisance Learners
make_grf_cate_learners(): Build GRF-Backed CATE Learners
make_grf_crr_learners(): Build GRF-Backed CRR Learners
Lrnr_cate_DR: Lrnr_cate_DR Class
Lrnr_cate_DR_selector: Lrnr_cate_DR_selector Class
Lrnr_cate_EP: Lrnr_cate_EP Class
Lrnr_cate_R: Lrnr_cate_R Class
Lrnr_cate_T: Lrnr_cate_T Class
Lrnr_crr_DR_selector: Lrnr_crr_DR_nonconvex Class
Lrnr_crr_EP: Lrnr_crr_EP Class
Lrnr_crr_IPW: Lrnr_crr_IPW Class
Lrnr_crr_T: Lrnr_crr_T Class
Lrnr_grf_causal_forest: GRF Causal Forest Learner
Lrnr_grf_forest: GRF Forest Learner
Lrnr_stratified_multivariate: Internal use. Converts a single outcome learner into a multivariate outcome learner that predicts a matrix of predictions obtained by evaluating the single outcome learner at each possible value of variable_stratify.
Lrnr_xgboost_early_stopping: XGBoost Learner with Early Stopping
cross_validate(): Cross-Validate Heterogeneous Treatment Effect Models
cross_validate_cate(): Cross-Validate CATE Models
cross_validate_crr(): Cross-Validate CRR Models
predict_hte3(): Predict Heterogeneous Treatment Effects using hte3 Learners
get_autoML(): Get Automated Machine Learning (AutoML) Learner
truncate_propensity(): Truncate and Calibrate Propensity Scores