postDoubleR (version 1.4.12)

nn_helper: Helper function for neural networks fitted by neuralnet

Description

Helper function that dispatches to neuralnet for the double ML estimation (see details).

Usage

nn_helper(X, Y, W, neural.net.Y = NULL, neural.net.W = NULL,
  standardize = TRUE, standardization.method = "min-max")

Arguments

X

A matrix of covariates (must be all numeric)

Y

A vector of the target variable, of same length as the number of rows of Y, must be numeric

W

A vector of the treatment variable, of same length as the number of rows of X, must be numeric

neural.net.Y

A model specification for Y, see neuralnet

neural.net.W

A model specification for W, see neuralnet

standardize

Whether to standardize the data before starting the computation, defaults to TRUE.

standardization.method

How to standardize data, defaults to min-max, also offers "Z-transform", "Unit-Scale" and "Mean-Scale"

Value

A list with two elements: The fitted W model and the fitted Y model.

Details

For a more steamlined usage, default arguments as implemented in the neuralnet package are passed to both networks during fitting, unless specified otherwise. Also, any attempt to set the formula or data arguments of neuralnet will be ignored and rewritted with internal structures. The function will print a warning if this happens.

Examples

# NOT RUN {
  n = 2000; p = 3
  X = matrix(rnorm(n*p), n, p)
  W = rbinom(n, 1, 0.4 + 0.2 * (X[,1] > 0))
  Y = rbinom(n, 1, 0.2 + 0.2 * (X[,2] > 0) + W * 0.1)

# note that this neural network can fail to converge

 nn_helper( X,
            Y,
            W,
            neural.net.W = list( act.fct = "logistic" ),
            neural.net.Y = list( act.fc = "logistic" ))
# }
# NOT RUN {

# }