nuisance_m: Cross-fitted ensemble prediction of outcome regression...

In MCKnaus/causalDML: Causal Double Machine Learning

Cross-fitted ensemble prediction of outcome regression nuisance parameter.

Description

Cross-fitted ensemble prediction of outcome regression nuisance parameter.

Usage

nuisance_m(
  ml,
  y,
  w_mat,
  x,
  cf_mat,
  cv = 5,
  weights = FALSE,
  path = NULL,
  quiet = TRUE
)

Arguments

ml	List of methods to be used in ensemble estimation of propensity score. Methods can be created by create_method.
y	Numerical vector containing the outcome variable.
w_mat	Logical matrix of treatment indicators (n x T+1). For example created by prep_w_mat.
x	Covariate matrix.
cf_mat	Logical matrix with k columns of indicators representing the different folds (for example created by prep_cf_mat).
cv	Number of cross-validation when estimating ensemble (default 5).
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 for later processing. Saved as Ensemble_Yi where i is the number of the treatment in multiple treatment settings.
quiet	If FALSE, ensemble estimators print method that is currently running.

Value

Returns n x T+1 matrix with each columns containing the predicted outcome for the treatment corresponding to w_mat.

MCKnaus/causalDML documentation built on Aug. 19, 2023, 5:47 p.m.