Package: DeepLearningCausal 0.0.107

Nguyen K. Huynh

DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks

Functions for deep learning estimation of Conditional Average Treatment Effects (CATEs) from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements the conformal prediction framework that enables computation and illustration of conformal prediction (CP) intervals for estimated individual treatment effects (ITEs) from meta-learner models. Additional functions in the package permit users to estimate the meta-learner CATEs and the PATT in settings with treatment noncompliance using weighted ensemble learning via the super learner approach and R neural networks.

Authors:Nguyen K. Huynh [aut, cre], Bumba Mukherjee [aut], Yang Yang [aut]

DeepLearningCausal_0.0.107.tar.gz
DeepLearningCausal_0.0.107.zip(r-4.7-any)DeepLearningCausal_0.0.107.zip(r-4.6-any)DeepLearningCausal_0.0.107.zip(r-4.5-any)
DeepLearningCausal_0.0.107.tgz(r-4.6-any)DeepLearningCausal_0.0.107.tgz(r-4.5-any)
DeepLearningCausal_0.0.107.tar.gz(r-4.7-any)DeepLearningCausal_0.0.107.tar.gz(r-4.6-any)
DeepLearningCausal_0.0.107.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
DeepLearningCausal/json (API)

# Install 'DeepLearningCausal' in R:
install.packages('DeepLearningCausal', repos = c('https://hknd23.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hknd23/deeplearningcausal/issues

Datasets:
  • exp_data - Survey Experiment of Support for Populist Policy
  • exp_data_full - Survey Experiment of Support for Populist Policy
  • pop_data - World Value Survey India Sample
  • pop_data_full - World Value Survey India Sample

On CRAN:

Conda:

causal-inferencedeep-neural-networksmachine-learning

4.79 score 4 stars 17 scripts 326 downloads 21 exports 130 dependencies

Last updated from:3e2779a56d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK187
source / vignettesOK207
linux-releaseOK186
macos-releaseOK210
macos-oldrelOK178
windows-develOK124
windows-releaseOK133
windows-oldrelOK122
wasm-releaseOK126

Exports:complier_modcomplier_predictconformal_plotdeep_complier_moddeep_predictdeep_response_modelhte_plotmetalearner_deeplearningmetalearner_ensemblemetalearner_neuralneuralnet_complier_modneuralnet_pattc_counterfactualsneuralnet_predictneuralnet_response_modelpattc_counterfactualspattc_deeplearningpattc_deeplearning_counterfactualspattc_ensemblepattc_neuralpython_readyresponse_model

Dependencies:backportsbase64encbitopsbslibcachemcaretcaToolscheckmateclasscliclockclustercodetoolscolorspaceconfigcpp11cvAUCdata.tableDerivdiagramdigestdottydplyre1071evaluatefarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygamgenericsggplot2globalsgluegowergplotsgridExtragtablegtoolshardhatherehighrHmischtmlTablehtmltoolshtmlwidgetsipredisobanditeratorsjquerylibjsonlitekeras3KernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemimeModelMetricsneuralnetnlmennetnnlsnumDerivparallellypillarpkgconfigplyrpngpROCprocessxprodlimprogressrproxypspurrrR6rappdirsRColorBrewerRcppRcppTOMLrecipesreshape2reticulaterlangrmarkdownROCRrpartrprojrootrstudioapiS7sassscalesshapesparsevctrsSQUAREMstringistringrSuperLearnersurvivaltensorflowtfautographtfrunstibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitewhiskerwithrxfunyamlzeallot

Readme and manuals

Help Manual

Help pageTopics
Train complier model using ensemble methodscomplier_mod
Complier model predictioncomplier_predict
conformal_plotconformal_plot
Train complier model using deep neural learning through Tensorflowdeep_complier_mod
Complier model predictiondeep_predict
Response model from experimental data using deep neural learning through Tensorflowdeep_response_model
Survey Experiment of Support for Populist Policyexp_data
Survey Experiment of Support for Populist Policyexp_data_full
hte_plothte_plot
metalearner_deeplearningmetalearner_deeplearning
metalearner_ensemblemetalearner_ensemble
metalearner_neuralmetalearner_neural
Train compliance model using neural networksneuralnet_complier_mod
Assess Population Data counterfactualsneuralnet_pattc_counterfactuals
Predicting Compliance from experimental dataneuralnet_predict
Modeling Responses from experimental data Using Deep NNneuralnet_response_model
Assess Population Data counterfactualspattc_counterfactuals
Deep PATT-Cpattc_deeplearning
Assess Population Data counterfactualspattc_deeplearning_counterfactuals
PATT-C SL Ensemblepattc_ensemble
Estimate PATT_C using Deep NNpattc_neural
plot.metalearner_deeplearningplot.metalearner_deeplearning
plot.metalearner_ensembleplot.metalearner_ensemble
plot.metalearner_neuralplot.metalearner_neural
plot.pattc_deeplearningplot.pattc_deeplearning
plot.pattc_ensembleplot.pattc_ensemble
plot.pattc_neuralplot.pattc_neural
World Value Survey India Samplepop_data
World Value Survey India Samplepop_data_full
print.metalearner_deeplearningprint.metalearner_deeplearning
print.metalearner_ensembleprint.metalearner_ensemble
print.metalearner_neuralprint.metalearner_neural
print.pattc_deeplearningprint.pattc_deeplearning
print.pattc_ensembleprint.pattc_ensemble
print.pattc_neuralprint.pattc_neural
Check for Python module availability and install if missing.python_ready
Response model from experimental data using SL ensembleresponse_model