nuisance_cf_grf: This function creates the nuisance parameters p(x), mu(x),...

In MCKnaus/CATEs: Conditional Average Treatment Effect with Causal Machine Learning

Description

This function creates the nuisance parameters p(x), mu(x), and mu_d(x) via cross-fitting using the grf package

Usage

nuisance_cf_grf(y, d, x, index, args_p = list(), args_y = list(),
  args_y0 = list(), args_y1 = list())

Arguments

y	Vector of outcome values
d	Vector of treament indicators
x	Matrix of covariates (N x p matrix)
index	List indicating indices for cross-fitting (e.g. obtained by createFolds of caret pkg)
args_p	List of arguments passed to estimate propensity score model
args_y	List of arguments passed to estimate outcome model
args_y0	List of arguments passed to estimate outcome model of non-treated
args_y1	List of arguments passed to estimate outcome model of treated

Value

Returns n x 4 matrix containing the nuisance parameters #'

MCKnaus/CATEs documentation built on Sept. 11, 2021, 10:12 p.m.