causalDML: This function estimates the average potential outcomes and...
In MCKnaus/causalDML: Causal Double Machine Learning

causalDML

R Documentation

This function estimates the average potential outcomes and average treatment effects using Double Machine Learning (DML).

Description

This function estimates the average potential outcomes and average treatment effects using Double Machine Learning (DML).

Usage

causalDML(
  y,
  w,
  x,
  ml_w = list(create_method("forest_grf")),
  ml_y = list(create_method("forest_grf")),
  cf = 5,
  cv = 5,
  cl = NULL,
  norm = 2,
  weights = FALSE,
  path = NULL,
  quiet = TRUE
)

Arguments

`y`	Numeric vector containing the outcome variable.
`w`	Treatment vector. Provide as factor to control ordering of the treatments, otherwise program orders treatments in ascending order or alphabetically.
`x`	Covariate matrix.
`ml_w`	List of methods to be used in ensemble estimation of propensity score. Methods can be created by `create_method`. Default is an untuned honest `regression_forest`.
`ml_y`	List of methods to be used in ensemble estimation of outcome regression. Methods can be created by `create_method`. Default is an untuned honest `regression_forest`.
`cf`	Number of cross-fitting folds for DML (default 5).
`cv`	Number of cross-validation folds when estimating ensemble if more than one method is defined in `ml_w` and/or `ml_y` (default 5).
`cl`	If not NULL, vector with cluster variables
`norm`	Controls normalization of IPW weights. 0: no normalization, 1: overall normalization, 2: normalization in each cross-fitting fold separately (default).
`weights`	If TRUE, prediction weights of the outcome nuisance extracted and saved (requires to provide a path).
`path`	Optional path to save the `ensemble` objects of each cross-fit for later inspection.
`quiet`	If FALSE, ensemble estimators print method that is currently running.