Package: DeepLearningCausal 0.0.103
Nguyen K. Huynh
DeepLearningCausal:Causal Inference with Super Learner and Deep Neural Networks
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:
DeepLearningCausal_0.0.103.tar.gz
DeepLearningCausal_0.0.103.tar.gz(r-4.5-noble)DeepLearningCausal_0.0.103.tar.gz(r-4.4-noble)
DeepLearningCausal_0.0.103.tgz(r-4.4-emscripten)DeepLearningCausal_0.0.103.tgz(r-4.3-emscripten)
DeepLearningCausal.pdf |DeepLearningCausal.html✨
DeepLearningCausal/json (API)
| # InstallDeepLearningCausal in R: |
| install.packages('DeepLearningCausal',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hknd23/deeplearningcausal/issues
- 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
Last updated 4 days agofrom:83961de18f
Exports:complier_modcomplier_predictmetalearner_deepneuralmetalearner_ensembleneuralnet_complier_modneuralnet_pattc_counterfactualsneuralnet_predictneuralnet_response_modelpattc_counterfactualspattc_deepneuralpattc_ensembleresponse_model
Dependencies:backportsbase64encbitbit64bitopsbootbroombslibcachemcaretcaToolscheckmateclassclicliprclockclustercodetoolscolorspacecpp11crayoncvAUCdata.tableDerivdiagramdigestdplyre1071ellipsisevaluatefansifarverfastmapfontawesomeforcatsforeachforeignFormulafsfuturefuture.applygamgbmgdatagenericsggplot2glmnetglobalsgluegowergplotsgridExtragtablegtoolshardhathavenhighrHmischmshtmlTablehtmltoolshtmlwidgetsipredisobanditeratorsjomojquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlme4lubridatemagrittrMASSMatrixmemoisemgcvmicemimeminqamitmlModelMetricsmunsellneuralnetnlmenloptrnnetnnlsnumDerivordinalpanparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrR6randomForestrappdirsRColorBrewerRcppRcppEigenreadrrecipesreshape2rlangrmarkdownROCRrpartrstudioapisassscalesshapeSQUAREMstringistringrSuperLearnersurvivaltibbletidyrtidyselecttimechangetimeDatetinytextzdbucminfutf8vctrsviridisviridisLitevroomweightswithrxfunxgboostyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Train complier model using ensemble methods | complier_mod |
| Complier model prediction | complier_predict |
| Survey Experiment of Support for Populist Policy | exp_data |
| Survey Experiment of Support for Populist Policy | exp_data_full |
| metalearner_deepneural | metalearner_deepneural |
| metalearner_ensemble | metalearner_ensemble |
| Train compliance model using neural networks | neuralnet_complier_mod |
| Assess Population Data counterfactuals | neuralnet_pattc_counterfactuals |
| Predicting Compliance from experimental data | neuralnet_predict |
| Modeling Responses from experimental data Using Deep NN | neuralnet_response_model |
| Assess Population Data counterfactuals | pattc_counterfactuals |
| Estimate PATT_C using Deep NN | pattc_deepneural |
| PATT_C SL Ensemble | pattc_ensemble |
| World Value Survey India Sample | pop_data |
| World Value Survey India Sample | pop_data_full |
| print.metalearner_ensemble | print.pattc_ensemble |
| Response model from experimental data using SL ensemble | response_model |