Package: ddml 0.9.0

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:10.1002/jae.3103>, 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_0.9.0.tar.gz
ddml_0.9.0.zip(r-4.7)ddml_0.9.0.zip(r-4.6)ddml_0.9.0.zip(r-4.5)
ddml_0.9.0.tgz(r-4.6-any)ddml_0.9.0.tgz(r-4.5-any)
ddml_0.9.0.tar.gz(r-4.7-any)ddml_0.9.0.tar.gz(r-4.6-any)
ddml_0.9.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ddml/json (API)
NEWS

# Install 'ddml' in R:

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

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

Pkgdown/docs site:https://thomaswiemann.com

Datasets:

AE98 - Random Subsample from the Data of Angrist & Evans

On CRAN:

6.04 score 24 stars 46 scripts 257 downloads 30 exports 19 dependencies

Last updated from:e17f94c638. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	NOTE	188
source / vignettes	OK	218
linux-release-x86_64	NOTE	195
macos-release-arm64	NOTE	152
macos-oldrel-arm64	NOTE	118
windows-devel	NOTE	139
windows-release	NOTE	182
windows-oldrel	NOTE	145
wasm-release	OK	138

Exports:crosspred crossval ddml ddml_apo ddml_ate ddml_att ddml_attgt ddml_fpliv ddml_late ddml_pliv ddml_plm ddml_policy ddml_rep ddml_replicate diagnostics ensemble ensemble_weights glance lincom lincom_weights_did mdl_bigGlm mdl_glm mdl_glmnet mdl_ranger mdl_xgboost ols ral ral_rep shortstacking tidy

Dependencies:codetools data.table foreach generics glmnet iterators jsonlite lattice MASS Matrix nnls pbapply quadprog ranger Rcpp RcppEigen shape survival xgboost

Get Started

Rendered fromddml.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2026-04-16
Started: 2023-01-27

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Random Subsample from the Data of Angrist & Evans (1998)	AE98
Split a DDML Object by Ensemble Type	as.list.ddml
Split a ddml_rep Object by Ensemble Type	as.list.ddml_rep
Split a RAL Object by Fit	as.list.ral
Split a RAL Rep Object by Fit	as.list.ral_rep
Extract Coefficients from a RAL Object	coef.ral
Extract Aggregated Coefficients	coef.ral_rep
Confidence Intervals for RAL Estimators	confint.ral
Confidence Intervals for RAL Rep Objects	confint.ral_rep
Cross-Fitted Predictions Using Stacking	crosspred
Estimator of the Mean Squared Prediction Error Using Cross-Validation	crossval
Construct a 'ddml' Object.	ddml
Estimator for the Average Potential Outcome	ddml_apo
Estimator for the Average Treatment Effect	ddml_ate ddml_att
Estimator for Group-Time Average Treatment Effects	ddml_attgt
Estimator for the Flexible Partially Linear IV Coefficient	ddml_fpliv
Estimator for the Local Average Treatment Effect	ddml_late
Estimator for the Partially Linear IV Coefficient	ddml_pliv
Estimator for the Partially Linear Regression Coefficient	ddml_plm
Estimator for the Multi-Action Policy Value	ddml_policy
Construct a Multi-Resample DDML Object	ddml_rep print.ddml_rep
Replicate a DDML Estimator Across Multiple Resamples	ddml_replicate
Intro to Double/Debiased Machine Learning	ddml-intro
Stacking Diagnostics for DDML Estimators	diagnostics
Stacking Estimator Using Combinations of Base Learners	ensemble
Compute Stacking Weights for Base Learners	ensemble_weights
Glance at a DDML Object	glance.ddml
Glance at a ddml_rep Object	glance.ddml_rep
Glance at a RAL Object	glance.ral
Glance at a RAL Rep Object	glance.ral_rep
Extract leverage (Hat Values)	hatvalues.ral
Linear Combinations of DDML Coefficients	lincom lincom.ddml lincom.ddml_rep print.lincom print.lincom_rep
Difference-in-Differences Aggregation Weights for lincom	lincom_weights_did
Wrapper for glmnet::bigGlm()	mdl_bigGlm
Wrapper for stats::glm()	mdl_glm
Wrapper for glmnet::glmnet()	mdl_glmnet
Wrapper for ranger::ranger()	mdl_ranger
Wrapper for xgboost::xgboost()	mdl_xgboost
Number of Observations in a RAL Object	nobs.ral
Ordinary Least Squares	ols
Plot Coefficients from a RAL Estimator	plot.ral plot.ral_rep
Predict Method for 'ensemble' Objects	predict.ensemble
Predict Method for mdl_bigGlm Objects	predict.mdl_bigGlm
Predict Method for mdl_glm Objects	predict.mdl_glm
Predict Method for mdl_glmnet Objects	predict.mdl_glmnet
Predict Method for mdl_ranger Objects	predict.mdl_ranger
Predict Method for mdl_xgboost Objects	predict.mdl_xgboost
Predict Method for ols Objects	predict.ols
Print Stacking Diagnostics	print.ddml_diagnostics
Construct a RAL Inference Object	ral
Construct a Replicated RAL Inference Object	ral_rep
Predictions using Short-Stacking	shortstacking
Summary for DDML Estimators	print.summary.ddml summary.ddml
Summary for ddml_rep Objects	print.summary.ddml_rep summary.ddml_rep
Summary for RAL Estimators	print.summary.ral summary.ral
Summary for RAL Rep Objects	print.summary.ral_rep summary.ral_rep
Tidy a DDML Object	tidy.ddml
Tidy Stacking Diagnostics	tidy.ddml_diagnostics
Tidy a ddml_rep Object	tidy.ddml_rep
Tidy a RAL Object	tidy.ral
Tidy a RAL Rep Object	tidy.ral_rep
Variance-Covariance Matrix for RAL Estimators	vcov.ral
Variance-Covariance Matrix for RAL Rep Objects	vcov.ral_rep