Description Usage Arguments Details Value Examples

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

1 2 |

`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" |

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.

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

1 2 3 4 5 6 7 8 9 10 11 12 | ```
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" ))
``` |

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