Package 'DeepLearningCausal' reference manual

Title:	Causal Inference with Super Learner and Deep Neural Networks
Description:	Functions to estimate Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT) from experimental or observational data using the Super Learner (SL) ensemble method and Deep neural networks. The package first provides functions to implement meta-learners such as the Single-learner (S-learner) and Two-learner (T-learner) described in Künzel et al. (2019) <doi:10.1073/pnas.1804597116> for estimating the CATE. The S- and T-learner are each estimated using the SL ensemble method and deep neural networks. It then provides functions to implement the Ottoboni and Poulos (2020) <doi:10.1515/jci-2018-0035> PATT-C estimator to obtain the PATT from experimental data with noncompliance by using the SL ensemble method and deep neural networks.
Authors:	Nguyen K. Huynh [aut, cre] , Bumba Mukherjee [aut] , Irvin (Chen-Yu) Lee [aut]
Maintainer:	Nguyen K. Huynh <[email protected]>
License:	GPL-3
Version:	0.0.104
Built:	2025-02-25 05:48:29 UTC
Source:	https://github.com/hknd23/deeplearningcausal

Train complier model using ensemble methods

Description

Train model using group exposed to treatment with compliance as binary outcome variable and covariates.

Usage

complier_mod(
  exp.data,
  complier.formula,
  treat.var,
  ID = NULL,
  SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet", "SL.glm")
)
complier_mod(
  exp.data,
  complier.formula,
  treat.var,
  ID = NULL,
  SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet", "SL.glm")
)

Arguments

`exp.data`	list object of experimental data.
`complier.formula`	formula to fit compliance model (c ~ x) using complier variable and covariates
`treat.var`	string specifying the binary treatment variable
`ID`	string for name of indentifier variable.
`SL.learners`	vector of strings for ML classifier algorithms. If left `NULL` employs extreme gradient boosting, elastic net regression, random forest, and neural nets.

Value

model object of trained model.

Complier model prediction

Description

Predict Compliance from control group in experimental data

Usage

complier_predict(complier.mod, exp.data, treat.var, compl.var)
complier_predict(complier.mod, exp.data, treat.var, compl.var)

Arguments

`complier.mod`	output from trained ensemble superlearner model
`exp.data`	`data.frame` object of experimental dataset
`treat.var`	string specifying the binary treatment variable
`compl.var`	string specifying binary complier variable

Value

data.frame object with true compliers, predicted compliers in the control group, and all compliers (actual + predicted).

Survey Experiment of Support for Populist Policy

Description

Shortened version of survey response data that incorporates a vignette survey experiment. The vignette describes an international crisis between country A and B. After reading this vignette, respondents are randomly assigned to the control group or to one of two treatments: policy prescription to said crisis by strong (populist) leader and centrist (non-populist) leader. The respondents are then asked whether they are willing to support the policy decision to fight a war against country A, which is the dependent variable.

Usage

data(exp_data)
data(exp_data)

Format

`exp_data`

A data frame with 257 rows and 12 columns:

female: Gender.
age: Age of participant.
income: Monthly household income.
religion: Religious denomination
practicing_religion: Importance of religion in life.
education: Educational level of participant.
political_ideology: Political ideology of participant.
employment: Employment status of participant.
marital_status: Marital status of participant.
job_loss: Concern about job loss.
strong_leader: Binary treatment measure of leader type.
support_war: Binary outcome measure for willingness to fight war.

#' ...

Source

Yadav and Mukherjee (2024)

Survey Experiment of Support for Populist Policy

Description

Extended experiment data with 514 observations

Usage

data(exp_data_full)
data(exp_data_full)

Format

`exp_data_full`

A data frame with 514 rows and 12 columns:

female: Gender.
age: Age of participant.
income: Monthly household income.
religion: Religious denomination
practicing_religion: Importance of religion in life.
education: Educational level of participant.
political_ideology: Political ideology of participant.
employment: Employment status of participant.
marital_status: Marital status of participant.
job_loss: Concern about job loss.
strong_leader: Binary treatment measure of leader type.
support_war: Binary outcome measure for willingness to fight war.

#' ...

Source

Yadav and Mukherjee (2024)

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
)
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)
                                  

# 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)

metalearner_ensemble

Description

metalearner_ensemble implements the S-learner and T-learner for estimating CATE using the super learner ensemble method. The super learner in this case includes the following machine learning algorithms: extreme gradient boosting, glmnet (elastic net regression), random forest and neural nets.

Usage

metalearner_ensemble(
  data,
  cov.formula,
  treat.var,
  meta.learner.type,
  SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet"),
  nfolds = 5,
  binary.outcome = FALSE
)
metalearner_ensemble(
  data,
  cov.formula,
  treat.var,
  meta.learner.type,
  SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet"),
  nfolds = 5,
  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.
`SL.learners`	vector for super learner ensemble that includes extreme gradient boosting, glmnet, random forest, and neural nets.
`nfolds`	number of folds for cross-validation. Currently supports up to 5 folds.
`binary.outcome`	logical specifying predicted outcome variable will take binary values or proportions.

Value

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

Examples

# load dataset
data(exp_data)
#load SuperLearner package
library(SuperLearner)
# estimate CATEs with S Learner
set.seed(123456)
slearner <- metalearner_ensemble(cov.formula = support_war ~ age +
                                  income + employed + job_loss,
                                data = exp_data,
                                treat.var = "strong_leader",
                                meta.learner.type = "S.Learner",
                                SL.learners = c("SL.glm"),
                                nfolds = 5,
                                binary.outcome = FALSE)
print(slearner)


# estimate CATEs with T Learner
set.seed(123456)
tlearner <- metalearner_ensemble(cov.formula = support_war ~ age + income +
                                  employed  + job_loss,
                                  data = exp_data,
                                  treat.var = "strong_leader",
                                  meta.learner.type = "T.Learner",
                                  SL.learners = c("SL.xgboost","SL.ranger",
                                               "SL.nnet"),
                                  nfolds = 5,
                                  binary.outcome = FALSE)

print(tlearner)
                                  

# load dataset
data(exp_data)
#load SuperLearner package
library(SuperLearner)
# estimate CATEs with S Learner
set.seed(123456)
slearner <- metalearner_ensemble(cov.formula = support_war ~ age +
                                  income + employed + job_loss,
                                data = exp_data,
                                treat.var = "strong_leader",
                                meta.learner.type = "S.Learner",
                                SL.learners = c("SL.glm"),
                                nfolds = 5,
                                binary.outcome = FALSE)
print(slearner)


# estimate CATEs with T Learner
set.seed(123456)
tlearner <- metalearner_ensemble(cov.formula = support_war ~ age + income +
                                  employed  + job_loss,
                                  data = exp_data,
                                  treat.var = "strong_leader",
                                  meta.learner.type = "T.Learner",
                                  SL.learners = c("SL.xgboost","SL.ranger",
                                               "SL.nnet"),
                                  nfolds = 5,
                                  binary.outcome = FALSE)

print(tlearner)

Train compliance model using neural networks

Description

Train model using group exposed to treatment with compliance as binary outcome variable and covariates.

Usage

neuralnet_complier_mod(
  complier.formula,
  exp.data,
  treat.var,
  algorithm = "rprop+",
  hidden.layer = c(4, 2),
  ID = NULL,
  stepmax = 1e+08
)
neuralnet_complier_mod(
  complier.formula,
  exp.data,
  treat.var,
  algorithm = "rprop+",
  hidden.layer = c(4, 2),
  ID = NULL,
  stepmax = 1e+08
)

Arguments

`complier.formula`	formula for complier variable as outcome and covariates (c ~ x)
`exp.data`	`data.frame` for experimental data.
`treat.var`	string for treatment variable.
`algorithm`	string for algorithm for training neural networks. Default set to the Resilient back propagation with weight backtracking (rprop+). Other algorithms include backprop', rprop-', 'sag', or 'slr' (see `neuralnet` package).
`hidden.layer`	vector for specifying hidden layers and number of neurons.
`ID`	string for identifier variable
`stepmax`	maximum number of steps.

Value

trained complier model object

Assess Population Data counterfactuals

Description

Create counterfactual datasets in the population for compliers and noncompliers. Then predict potential outcomes using trained model from neuralnet_response_model.

Usage

neuralnet_pattc_counterfactuals(
  pop.data,
  neuralnet.response.mod,
  ID = NULL,
  cluster = NULL,
  binary.outcome = FALSE
)
neuralnet_pattc_counterfactuals(
  pop.data,
  neuralnet.response.mod,
  ID = NULL,
  cluster = NULL,
  binary.outcome = FALSE
)

Arguments

`pop.data`	population data.
`neuralnet.response.mod`	trained model from. `neuralnet_response_model`.
`ID`	string for identifier variable.
`cluster`	string for clustering variable (currently unused).
`binary.outcome`	logical specifying predicted outcome variable will take binary values or proportions.

Value

data.frame of predicted outcomes of response variable from counterfactuals.

Predicting Compliance from experimental data

Description

Predicting Compliance from control group experimental data

Usage

neuralnet_predict(neuralnet.complier.mod, exp.data, treat.var, compl.var)
neuralnet_predict(neuralnet.complier.mod, exp.data, treat.var, compl.var)

Arguments

`neuralnet.complier.mod`	results from `neuralnet_complier_mod`
`exp.data`	`data.frame` of experimental data
`treat.var`	string for treatment variable
`compl.var`	string for compliance variable

Value

data.frame object with true compliers, predicted compliers in the control group, and all compliers (actual + predicted).

Modeling Responses from experimental data Using Deep NN

Description

Model Responses from all compliers (actual + predicted) in experimental data using neural network.

Usage

neuralnet_response_model(
  response.formula,
  exp.data,
  neuralnet.compliers,
  compl.var,
  algorithm = "rprop+",
  hidden.layer = c(4, 2),
  stepmax = 1e+08
)
neuralnet_response_model(
  response.formula,
  exp.data,
  neuralnet.compliers,
  compl.var,
  algorithm = "rprop+",
  hidden.layer = c(4, 2),
  stepmax = 1e+08
)

Arguments

`response.formula`	formula for response variable and covariates (y ~ x)
`exp.data`	`data.frame` of experimental data.
`neuralnet.compliers`	`data.frame` of compliers (actual + predicted) from `neuralnet_predict`.
`compl.var`	string of compliance variable
`algorithm`	neural network algorithm, default set to `"rprop+"`.
`hidden.layer`	vector specifying hidden layers and number of neurons.
`stepmax`	maximum number of steps for training model.

Value

trained response model object

Assess Population Data counterfactuals

Description

Create counterfactual datasets in the population for compliers and noncompliers. Then predict potential outcomes from counterfactuals.

Usage

pattc_counterfactuals(
  pop.data,
  response.mod,
  ID = NULL,
  cluster = NULL,
  binary.outcome = FALSE
)
pattc_counterfactuals(
  pop.data,
  response.mod,
  ID = NULL,
  cluster = NULL,
  binary.outcome = FALSE
)

Arguments

`pop.data`	population dataset
`response.mod`	trained model from `response_model`.
`ID`	string fir identifier variable
`cluster`	string for clustering variable
`binary.outcome`	logical specifying whether predicted outcomes are proportions or binary (0-1).

Value

data.frame object of predicted outcomes of counterfactual groups.

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
)
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`	`data.frame` of experimental data. Must include binary treatment and compliance variables.
`pop.data`	`data.frame` of population data. Must include binary compliance variable
`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 `"rprop+"`. See (`neuralnet` package for available algorithms).
`response.algorithm`	string for algorithim to train neural network for response model. Default set to `"rprop+"`. See (`neuralnet` package for available algorithms).
`compl.hidden.layer`	vector for specifying hidden layers and number of neurons in complier model.
`response.hidden.layer`	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)



# 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)

PATT_C SL Ensemble

Description

pattc_ensemble estimates the Population Average Treatment Effect of the Treated from experimental data with noncompliers using the super learner ensemble that includes extreme gradient boosting, glmnet (elastic net regression), random forest and neural nets.

Usage

pattc_ensemble(
  response.formula,
  exp.data,
  pop.data,
  treat.var,
  compl.var,
  compl.SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet", "SL.glm"),
  response.SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet", "SL.glm"),
  ID = NULL,
  cluster = NULL,
  binary.outcome = FALSE,
  bootstrap = FALSE,
  nboot = 1000
)
pattc_ensemble(
  response.formula,
  exp.data,
  pop.data,
  treat.var,
  compl.var,
  compl.SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet", "SL.glm"),
  response.SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet", "SL.glm"),
  ID = NULL,
  cluster = NULL,
  binary.outcome = FALSE,
  bootstrap = FALSE,
  nboot = 1000
)

Arguments

`response.formula`	formula for the effects of covariates on outcome variable (y ~ x).
`exp.data`	`data.frame` object for experimental data. Must include binary treatment and compliance variable.
`pop.data`	`data.frame` object for population data. Must include binary compliance variable.
`treat.var`	string for binary treatment variable.
`compl.var`	string for binary compliance variable.
`compl.SL.learners`	vector of names of ML algorithms used for compliance model.
`response.SL.learners`	vector of names of ML algorithms used for response model.
`ID`	string for name of identifier. (currently not used)
`cluster`	string for name of cluster variable. (currently not used)
`binary.outcome`	logical specifying predicted outcome variable will take binary values or proportions.
`bootstrap`	logical for bootstrapped PATT-C.
`nboot`	number of bootstrapped samples. Only used with `bootstrap = FALSE`

Value

pattc_ensemble object of results of t test as PATTC estimate.

Examples


# load datasets
data(exp_data_full) # full experimental data
data(exp_data) #experimental data
data(pop_data) #population data
#attach SuperLearner (model will not recognize learner if package is not loaded)
library(SuperLearner)
set.seed(123456)
#specify models and estimate PATTC
pattc <- pattc_ensemble(response.formula = support_war ~ age + income +
                                education + employed + job_loss,
                                exp.data = exp_data_full,
                                pop.data = pop_data,
                                treat.var = "strong_leader",
                                compl.var = "compliance",
                                compl.SL.learners = c("SL.glm", "SL.nnet"),
                                response.SL.learners = c("SL.glm", "SL.nnet"),
                                ID = NULL,
                                cluster = NULL,
                                binary.outcome = FALSE)

print(pattc)

pattc_boot <- pattc_ensemble(response.formula = support_war ~ age + income +
                                education + employed + job_loss,
                                exp.data = exp_data_full,
                                pop.data = pop_data,
                                treat.var = "strong_leader",
                                compl.var = "compliance",
                                compl.SL.learners = c("SL.glm", "SL.nnet"),
                                response.SL.learners = c("SL.glm", "SL.nnet"),
                                ID = NULL,
                                cluster = NULL,
                                binary.outcome = FALSE,
                                bootstrap = TRUE,
                                nboot = 1000)
print(pattc_boot)


# load datasets
data(exp_data_full) # full experimental data
data(exp_data) #experimental data
data(pop_data) #population data
#attach SuperLearner (model will not recognize learner if package is not loaded)
library(SuperLearner)
set.seed(123456)
#specify models and estimate PATTC
pattc <- pattc_ensemble(response.formula = support_war ~ age + income +
                                education + employed + job_loss,
                                exp.data = exp_data_full,
                                pop.data = pop_data,
                                treat.var = "strong_leader",
                                compl.var = "compliance",
                                compl.SL.learners = c("SL.glm", "SL.nnet"),
                                response.SL.learners = c("SL.glm", "SL.nnet"),
                                ID = NULL,
                                cluster = NULL,
                                binary.outcome = FALSE)

print(pattc)

pattc_boot <- pattc_ensemble(response.formula = support_war ~ age + income +
                                education + employed + job_loss,
                                exp.data = exp_data_full,
                                pop.data = pop_data,
                                treat.var = "strong_leader",
                                compl.var = "compliance",
                                compl.SL.learners = c("SL.glm", "SL.nnet"),
                                response.SL.learners = c("SL.glm", "SL.nnet"),
                                ID = NULL,
                                cluster = NULL,
                                binary.outcome = FALSE,
                                bootstrap = TRUE,
                                nboot = 1000)
print(pattc_boot)

World Value Survey India Sample

Description

World Value Survey (WVS) Data for India in 2022. The variables drawn from the said WVS India data match the covariates from the India survey experiment sample.

Usage

data(pop_data)
data(pop_data)

Format

`pop_data`

A data frame with 846 rows and 13 columns:

female: Respondent’s Sex.
age: Age of respondent.
income: income group of Household.
religion: Religious denomination
practicing_religion: Importance of religion in respondent’s life.
education: Educational level of respondent.
political_ideology: Political ideology of respondent.
employment: Employment status and full-time employee.
marital_status: Marital status of respondent.
job_loss: Concern about job loss.
support_war: Binary (Yes/No) outcome measure for willingness to fight war.
strong_leader: Binary measure of preference for strong leader.

...

Source

Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2020. World Values Survey: Round Seven – Country-Pooled Datafile. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. <doi.org/10.14281/18241.1>

World Value Survey India Sample

Description

Extended World Value Survey (WVS) Data for India in 1995, 2001, 2006, 2012, and 2022.

Usage

data(pop_data_full)
data(pop_data_full)

Format

`pop_data_full`

A data frame with 11,813 rows and 13 columns:

female: Respondent’s Sex.
age: Age of respondent.
income: income group of Household.
religion: Religious denomination
practicing_religion: Importance of religion in respondent’s life.
education: Educational level of respondent.
political_ideology: Political ideology of respondent.
employment: Employment status and full-time employee.
marital_status: Marital status of respondent.
job_loss: Concern about job loss.
support_war: Binary (Yes/No) outcome measure for willingness to fight war.
strong_leader: Binary measure of preference for strong leader.

...

Source

print.metalearner_deepneural

Description

Print method for metalearner_deepneural

Usage

## S3 method for class 'metalearner_deepneural'
print(x, ...)
## S3 method for class 'metalearner_deepneural'
print(x, ...)

Arguments

`x`	`metalearner_deepneural` class object from `metalearner_deepneural`
`...`	additional parameter

Value

list of model results

print.metalearner_ensemble

Description

Print method for metalearner_ensemble

Usage

## S3 method for class 'metalearner_ensemble'
print(x, ...)
## S3 method for class 'metalearner_ensemble'
print(x, ...)

Arguments

`x`	`metalearner_ensemble` class object from `metalearner_ensemble`
`...`	additional parameter

Value

list of model results

print.pattc_deepneural

Description

Print method for pattc_deepneural

Usage

## S3 method for class 'pattc_deepneural'
print(x, ...)
## S3 method for class 'pattc_deepneural'
print(x, ...)

Arguments

`x`	`pattc_deepneural` class object from `pattc_deepneural`
`...`	additional parameter

Value

list of model results

print.pattc_ensemble

Description

Print method for pattc_ensemble

Usage

## S3 method for class 'pattc_ensemble'
print(x, ...)
## S3 method for class 'pattc_ensemble'
print(x, ...)

Arguments

`x`	`pattc_ensemble` class object from `pattc_ensemble`
`...`	additional parameter

Value

list of model results

Response model from experimental data using SL ensemble

Description

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

Usage

response_model(
  response.formula,
  exp.data,
  compl.var,
  exp.compliers,
  family = "binomial",
  ID = NULL,
  SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet", "SL.glm")
)
response_model(
  response.formula,
  exp.data,
  compl.var,
  exp.compliers,
  family = "binomial",
  ID = NULL,
  SL.learners = c("SL.glmnet", "SL.xgboost", "SL.ranger", "SL.nnet", "SL.glm")
)

Arguments

`response.formula`	formula to fit the response model (y ~ x) using binary outcome variable and covariates
`exp.data`	experimental dataset.
`compl.var`	string specifying binary complier variable
`exp.compliers`	`data.frame` object of compliers from `complier_predict`.
`family`	string for `"gaussian"` or `"binomial"`.
`ID`	string for identifier variable.
`SL.learners`	vector of names of ML algorithms used for ensemble model.

Value

trained response model.

Package 'DeepLearningCausal'

Help Index

Train complier model using ensemble methods

Description

Usage

Arguments

Value

Complier model prediction

Description

Usage

Arguments

Value

Survey Experiment of Support for Populist Policy

Description

Usage

Format

exp_data

Source

Survey Experiment of Support for Populist Policy

Description

Usage

Format

exp_data_full

Source

metalearner_deepneural

Description

Usage

Arguments

Value

Examples

metalearner_ensemble

Description

Usage

Arguments

Value

Examples

Train compliance model using neural networks

Description

Usage

Arguments

Value

Assess Population Data counterfactuals

Description

Usage

Arguments

Value

Predicting Compliance from experimental data

Description

Usage

Arguments

Value

Modeling Responses from experimental data Using Deep NN

Description

Usage

Arguments

Value

Assess Population Data counterfactuals

Description

Usage

Arguments

Value

Estimate PATT_C using Deep NN

Description

Usage

Arguments

Value

Examples

PATT_C SL Ensemble

Description

Usage

Arguments

Value

Examples

World Value Survey India Sample

Description

Usage

Format

pop_data

Source

World Value Survey India Sample

`exp_data`

`exp_data_full`

`pop_data`

`pop_data_full`