Tidy Estimation of Heterogeneous Treatment Effects
Add an additional diagnostic to the effect model
Add an additional model to the joint effect ensemble
Uses a known propensity score
Adds moderators to the configuration
Add an additional diagnostic to the outcome model
Add an additional model to the outcome ensemble
Add an additional diagnostic to the propensity score
Add an additional model to the propensity score ensemble
Adds variable importance information
Attach an HTE_cfg to a dataframe
Create a basic config for HTE estimation
Calculates a SATE and a PATE using AIPW
Calculate diagnostics
Calculate Linear Variable Importance of HTEs
Calculate "partial" CATE estimates
Regression ROC Curve calculation
Calculate Variable Importance of HTEs
Checks that a dataframe has an attached configuration for HTEs
Checks that an appropriate identifier has been provided
Checks that nuisance models have been estimated and exist in the suppl...
Checks that splits have been properly created.
Checks that an appropriate weighting variable has been provided
Configuration of a Constant Estimator
Construct Pseudo-outcomes
Configuration of Model Diagnostics
Function to calculate diagnostics based on model outputs
Estimate Quantities of Interest
Fits a treatment effect model using the appropriate settings
Fit a predictor for treatment effects
Fits a plugin model using the appropriate settings
Fits a propensity score model using the appropriate settings
Fits a T-learner using the appropriate settings
Predictor class for the cross-fit predictor of "partial" CATEs
Configuration of Quantities of Interest
R6 class to represent partitions of the data between training and held...
Configuration for a Kernel Smoother
Configuration of Known Model
Removes rows which have missing data on any of the supplied columns.
Define splits for cross-fitting
Configuration of Marginal CATEs
Base Class of Model Configurations
R6 class to represent data to be used in estimating a model
Configuration of Partial CATEs
Prediction for an SL.glmnet object
Estimate models of nuisance functions
Configuration of Quantities of Interest
Removes variable importance information
Elastic net regression with pairwise interactions
Configuration for a SuperLearner Ensemble
Configuration of SuperLearner Submodel
Partition the data into folds
Configuration for a Stratification Estimator
tidyhte: Tidy Estimation of Heterogeneous Treatment Effects
Configuration of Variable Importance
Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. You provide a simple recipe for what machine learning algorithms to use in estimating the nuisance functions and 'tidyhte' will take care of cross-validation, estimation, model selection, diagnostics and construction of relevant quantities of interest about the variability of treatment effects.