metalearner_deepneural {DeepLearningCausal}

metalearner_deepneural

Description

metalearner_deepneural implements the S-learner and T-learner for estimating CATE using Deep Neural Networks. The Resilient back propagation (Rprop) algorithm is used for training neural networks.

Usage

metalearner_deepneural(
  data,
  cov.formula,
  treat.var,
  meta.learner.type,
  stepmax = 1e+05,
  nfolds = 5,
  algorithm = "rprop+",
  hidden.layer = c(4, 2),
  linear.output = FALSE,
  binary.outcome = FALSE
)

Arguments

data

data.frame object of data.

cov.formula

formula description of the model y ~ x(list of covariates).

treat.var

string for the name of treatment variable.

meta.learner.type

string specifying is the S-learner and "T.Learner" for the T-learner model.

stepmax

maximum number of steps for training model.

nfolds

number of folds for cross-validation. Currently supports up to 5 folds.

algorithm

a string for the algorithm for the neural network. Default set to ⁠rprop+⁠, the Resilient back propagation (Rprop) with weight backtracking algorithm for training neural networks.

hidden.layer

vector of integers specifying layers and number of neurons.

linear.output

logical specifying regression (TRUE) or classification (FALSE) model.

binary.outcome

logical specifying predicted outcome variable will take binary values or proportions.

Value

metalearner_deepneural of predicted outcome values and CATEs estimated by the meta learners for each observation.

Examples

# load dataset
data(exp_data)
# estimate CATEs with S Learner
set.seed(123456)
slearner_nn <- metalearner_deepneural(cov.formula = support_war ~ age + income +
                                   employed  + job_loss,
                                   data = exp_data,
                                   treat.var = "strong_leader",
                                   meta.learner.type = "S.Learner",
                                   stepmax = 2e+9,
                                   nfolds = 5,
                                   algorithm = "rprop+",
                                   hidden.layer = c(1),
                                   linear.output = FALSE,
                                   binary.outcome = FALSE)

print(slearner_nn)

# load dataset
set.seed(123456)
# estimate CATEs with T Learner
tlearner_nn <- metalearner_deepneural(cov.formula = support_war ~ age +
                                  income  +
                                  employed  + job_loss,
                                  data = exp_data,
                                  treat.var = "strong_leader",
                                  meta.learner.type = "T.Learner",
                                  stepmax = 1e+9,
                                  nfolds = 5,
                                  algorithm = "rprop+",
                                  hidden.layer = c(2,1),
                                  linear.output = FALSE,
                                  binary.outcome = FALSE)

print(tlearner_nn)