nuisance_m: Cross-fitted ensemble prediction of outcome regression...
In MCKnaus/causalDML: Causal Double Machine Learning

nuisance_m

R Documentation

Cross-fitted ensemble prediction of outcome regression nuisance parameter.

Cross-fitted ensemble prediction of outcome regression nuisance parameter.

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

`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.