pattc_deepneural {DeepLearningCausal} | R Documentation |
Estimate PATT_C using Deep NN
Description
estimates the Population Average Treatment Effect of the Treated from experimental data with noncompliers using Deep Neural Networks.
Usage
pattc_deepneural(
response.formula,
exp.data,
pop.data,
treat.var,
compl.var,
compl.algorithm = "rprop+",
response.algorithm = "rprop+",
compl.hidden.layer = c(4, 2),
response.hidden.layer = c(4, 2),
compl.stepmax = 1e+08,
response.stepmax = 1e+08,
ID = NULL,
cluster = NULL,
binary.outcome = FALSE,
bootstrap = FALSE,
nboot = 1000
)
Arguments
response.formula |
formula of response variable as outcome and covariates (y ~ x) |
exp.data |
|
pop.data |
|
treat.var |
string for treatment variable. |
compl.var |
string for compliance variable |
compl.algorithm |
string for algorithim to train neural network for
compliance model. Default set to |
response.algorithm |
string for algorithim to train neural network for
response model. Default set to |
vector for specifying hidden layers and number of neurons in complier model. | |
vector for specifying hidden layers and number of neurons in response model. | |
compl.stepmax |
maximum number of steps for complier model |
response.stepmax |
maximum number of steps for response model |
ID |
string for identifier variable |
cluster |
string for cluster variable. |
binary.outcome |
logical specifying predicted outcome variable will take binary values or proportions. |
bootstrap |
logical for bootstrapped PATT-C. |
nboot |
number of bootstrapped samples |
Value
pattc_deepneural
class object of results of t test as PATTC estimate.
Examples
# load datasets
data(exp_data) #experimental data
data(pop_data) #population data
# specify models and estimate PATTC
set.seed(123456)
pattc_neural <- pattc_deepneural(response.formula = support_war ~ age + female +
income + education + employed + married +
hindu + job_loss,
exp.data = exp_data,
pop.data = pop_data,
treat.var = "strong_leader",
compl.var = "compliance",
compl.algorithm = "rprop+",
response.algorithm = "rprop+",
compl.hidden.layer = c(4,2),
response.hidden.layer = c(4,2),
compl.stepmax = 1e+09,
response.stepmax = 1e+09,
ID = NULL,
cluster = NULL,
binary.outcome = FALSE)
print(pattc_neural)
pattc_neural_boot <- pattc_deepneural(response.formula = support_war ~ age + female +
income + education + employed + married +
hindu + job_loss,
exp.data = exp_data,
pop.data = pop_data,
treat.var = "strong_leader",
compl.var = "compliance",
compl.algorithm = "rprop+",
response.algorithm = "rprop+",
compl.hidden.layer = c(4,2),
response.hidden.layer = c(4,2),
compl.stepmax = 1e+09,
response.stepmax = 1e+09,
ID = NULL,
cluster = NULL,
binary.outcome = FALSE,
bootstrap = TRUE,
nboot = 2000)
print(pattc_neural_boot)