Tidy Estimation of Heterogeneous Treatment Effects

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Documentation for package ‘tidyhte’ version 1.0.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

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
Constant_cfg	Configuration of a Constant Estimator
construct_pseudo_outcomes	Construct Pseudo-outcomes
Diagnostics_cfg	Configuration of Model Diagnostics
estimate_QoI	Estimate Quantities of Interest
HTE_cfg	Configuration of Quantities of Interest
KernelSmooth_cfg	Configuration for a Kernel Smoother
Known_cfg	Configuration of Known Model
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
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
SL.glmnet.interaction	Elastic net regression with pairwise interactions
SLEnsemble_cfg	Configuration for a SuperLearner Ensemble
SLLearner_cfg	Configuration of SuperLearner Submodel
Stratified_cfg	Configuration for a Stratification Estimator
VIMP_cfg	Configuration of Variable Importance