Package: ddml 0.2.2

Thomas Wiemann

ddml:Double/Debiased Machine Learning

Estimate common causal parameters using double/debiased machine learning as proposed by Chernozhukov et al. (2018) <doi:10.1111/ectj.12097>. 'ddml' simplifies estimation based on (short-)stacking as discussed in Ahrens et al. (2024) <doi:10.1177/1536867X241233641>, which leverages multiple base learners to increase robustness to the underlying data generating process.

Authors:Achim Ahrens [aut], Christian B Hansen [aut], Mark E Schaffer [aut], Thomas Wiemann [aut, cre]

ddml_0.2.2.tar.gz
ddml_0.2.2.tar.gz(r-4.5-noble)ddml_0.2.2.tar.gz(r-4.4-noble)
ddml_0.2.2.tgz(r-4.4-emscripten)ddml_0.2.2.tgz(r-4.3-emscripten)
ddml.pdf |ddml.html✨
ddml/json (API)
NEWS

# Installddml in R:

install.packages('ddml',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/thomaswiemann/ddml/issues

Datasets:

AE98 - Random subsample from the data of Angrist & Evans (1991).

14 exports 0.49 score 75 dependencies 329 downloads

Last updated 9 days agofrom:702d41ee2c

Exports:crosspred crossval ddml_ate ddml_att ddml_fpliv ddml_late ddml_pliv ddml_plm mdl_glm mdl_glmnet mdl_ranger mdl_xgboost ols shortstacking

Dependencies:abind AER backports boot broom car carData cli codetools colorspace cowplot cpp11 data.table Deriv doBy dplyr fansi farver foreach Formula generics ggplot2 glmnet glue gtable isoband iterators jsonlite labeling lattice lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels mgcv microbenchmark minqa modelr munsell nlme nloptr nnet nnls numDeriv pbkrtest pillar pkgconfig purrr quadprog quantreg R6 ranger RColorBrewer Rcpp RcppEigen rlang sandwich scales shape SparseM stringi stringr survival tibble tidyr tidyselect utf8 vctrs viridisLite withr xgboost zoo

Get Started

Rendered fromddml.Rmdusingknitr::rmarkdownon Jun 27 2024.

Last update: 2024-06-27
Started: 2023-08-29

Citation

Readme and manuals

Help Manual

Help page	Topics
Random subsample from the data of Angrist & Evans (1991).	AE98
Cross-Predictions using Stacking.	crosspred
Estimator of the Mean Squared Prediction Error using Cross-Validation.	crossval
ddml: Double/Debiased Machine Learning in R	ddml
Estimators of Average Treatment Effects.	ddml_ate ddml_att
Estimator for the Flexible Partially Linear IV Model.	ddml_fpliv
Estimator of the Local Average Treatment Effect.	ddml_late
Estimator for the Partially Linear IV Model.	ddml_pliv
Estimator for the Partially Linear Model.	ddml_plm
Wrapper for 'stats::glm()'.	mdl_glm
Wrapper for 'glmnet::glmnet()'.	mdl_glmnet
Wrapper for 'ranger::ranger()'.	mdl_ranger
Wrapper for 'xgboost::xgboost()'.	mdl_xgboost
Ordinary least squares.	ols
Print Methods for Treatment Effect Estimators.	print.summary.ddml_ate print.summary.ddml_att print.summary.ddml_late
Print Methods for Treatment Effect Estimators.	print.summary.ddml_fpliv print.summary.ddml_pliv print.summary.ddml_plm
Predictions using Short-Stacking.	shortstacking
Inference Methods for Treatment Effect Estimators.	summary.ddml_ate summary.ddml_att summary.ddml_late
Inference Methods for Partially Linear Estimators.	summary.ddml_fpliv summary.ddml_pliv summary.ddml_plm