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

DML_aipw

R Documentation

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

Description

More recent version of causalDML with more functions and especially a more precise function name.

Usage

DML_aipw(
  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,
  e_mat = NULL,
  m_mat = NULL,
  cf_mat = NULL
)

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.
`e_mat`	Optional n x T+1 matrix with propensity scores calculated outside of function.
`m_mat`	Optional n x T+1 matrix fitted outcome values calculated outside of function.
`cf_mat`	Optional prespecified logical matrix with k columns of indicators representing the different folds (for example created by `prep_cf_mat`).