Man pages for tidyhte
Tidy Estimation of Heterogeneous Treatment Effects
| add_effect_diagnostic | Add an additional diagnostic to the effect model |
| add_effect_model | Add an additional model to the joint effect ensemble |
| add_known_propensity_score | Uses a known propensity score |
| add_moderator | Adds moderators to the configuration |
| add_outcome_diagnostic | Add an additional diagnostic to the outcome model |
| add_outcome_model | Add an additional model to the outcome ensemble |
| add_propensity_diagnostic | Add an additional diagnostic to the propensity score |
| add_propensity_score_model | Add an additional model to the propensity score ensemble |
| add_vimp | Adds variable importance information |
| attach_config | Attach an 'HTE_cfg' to a dataframe |
| basic_config | Create a basic config for HTE estimation |
| calculate_ate | Calculates a SATE and a PATE using AIPW |
| calculate_diagnostics | Calculate diagnostics |
| calculate_linear_vimp | Calculate Linear Variable Importance of HTEs |
| calculate_pcate_quantities | Calculate "partial" CATE estimates |
| calculate_rroc | Regression ROC Curve calculation |
| calculate_vimp | Calculate Variable Importance of HTEs |
| check_data_has_hte_cfg | Checks that a dataframe has an attached configuration for... |
| check_identifier | Checks that an appropriate identifier has been provided |
| check_nuisance_models | Checks that nuisance models have been estimated and exist in... |
| check_splits | Checks that splits have been properly created. |
| check_weights | Checks that an appropriate weighting variable has been... |
| Constant_cfg | Configuration of a Constant Estimator |
| construct_pseudo_outcomes | Construct Pseudo-outcomes |
| Diagnostics_cfg | Configuration of Model Diagnostics |
| estimate_diagnostic | Function to calculate diagnostics based on model outputs |
| estimate_QoI | Estimate Quantities of Interest |
| fit_effect | Fits a treatment effect model using the appropriate settings |
| fit_fx_predictor | Fit a predictor for treatment effects |
| fit_plugin | Fits a plugin model using the appropriate settings |
| fit_plugin_A | Fits a propensity score model using the appropriate settings |
| fit_plugin_Y | Fits a T-learner using the appropriate settings |
| FX.Predictor | Predictor class for the cross-fit predictor of "partial"... |
| HTE_cfg | Configuration of Quantities of Interest |
| HTEFold | R6 class to represent partitions of the data between training... |
| KernelSmooth_cfg | Configuration for a Kernel Smoother |
| Known_cfg | Configuration of Known Model |
| listwise_deletion | Removes rows which have missing data on any of the supplied... |
| make_splits | Define splits for cross-fitting |
| MCATE_cfg | Configuration of Marginal CATEs |
| Model_cfg | Base Class of Model Configurations |
| Model_data | R6 class to represent data to be used in estimating a model |
| PCATE_cfg | Configuration of Partial CATEs |
| predict.SL.glmnet.interaction | Prediction for an SL.glmnet object |
| produce_plugin_estimates | Estimate models of nuisance functions |
| QoI_cfg | Configuration of Quantities of Interest |
| remove_vimp | Removes variable importance information |
| SLEnsemble_cfg | Configuration for a SuperLearner Ensemble |
| SL.glmnet.interaction | Elastic net regression with pairwise interactions |
| SLLearner_cfg | Configuration of SuperLearner Submodel |
| split_data | Partition the data into folds |
| Stratified_cfg | Configuration for a Stratification Estimator |
| tidyhte-package | tidyhte: Tidy Estimation of Heterogeneous Treatment Effects |
| VIMP_cfg | Configuration of Variable Importance |
