deep_response_model: Response model from experimental data using deep neural...

In DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks

Response model from experimental data using deep neural learning through Tensorflow

Description

Train response model (response variable as outcome and covariates) from all compliers (actual + predicted) in experimental data using Tensorflow.

Usage

deep_response_model(
  response.formula,
  exp.data,
  exp.compliers,
  compl.var,
  algorithm = "adam",
  hidden.layer = c(2, 2),
  hidden_activation = "relu",
  epoch = 10,
  verbose = 1,
  batch_size = 32,
  output_units = 1,
  validation_split = NULL,
  patience = NULL,
  output_activation = "linear",
  loss = "mean_squared_error",
  metrics = "mean_squared_error",
  dropout_rate = NULL
)

Arguments

response.formula	formula specifying the response variable and covariates.
exp.data	experimental dataset.
exp.compliers	data.frame object of compliers from complier_predict.
compl.var	string specifying binary complier variable
algorithm	string for optimizer algorithm in response model.
hidden.layer	vector specifying hidden layers and the number of neurons in each hidden layer
hidden_activation	string or vector for activation functions in hidden layers.
epoch	integer for number of epochs
verbose	1 to display model training information and learning curve plot. 0 to suppress messages and plots.
batch_size	batch size to split training data.
output_units	integer for units in output layer. Defaults to 1 for continuous and binary outcome variables. In case of multinomial outcome variable, value should be set to the number of categories.
validation_split	double for the proportion of test data to be split as validation in response model.
patience	integer for number of epochs with no improvement after which training will be stopped.
output_activation	string for activation function in output layer. "linear" is recommended for continuous outcome variables, and "sigmoid" for binary outcome variables
loss	string for loss function. "mean_squared_error" recommended for linear models, "binary_crossentropy" for binary models.
metrics	string for metrics. "mean_squared_error" recommended for linear models, "binary_accuracy" for binary models.
dropout_rate	double or vector for proportion of hidden layer to drop out in response model.

Value

model object of trained response model.

DeepLearningCausal documentation built on Nov. 6, 2025, 5:08 p.m.