Double/Debiased Machine Learning
Cross-Predictions using Stacking.
Estimator of the Mean Squared Prediction Error using Cross-Validation.
Estimators of Average Treatment Effects.
Estimator for the Flexible Partially Linear IV Model.
Estimator of the Local Average Treatment Effect.
Estimator for the Partially Linear IV Model.
Estimator for the Partially Linear Model.
ddml: Double/Debiased Machine Learning in R
Wrapper for stats::glm().
Wrapper for glmnet::glmnet().
Wrapper for ranger::ranger().
Wrapper for xgboost::xgboost().
Ordinary least squares.
Print Methods for Treatment Effect Estimators.
Print Methods for Treatment Effect Estimators.
Predictions using Short-Stacking.
Inference Methods for Treatment Effect Estimators.
Inference Methods for Partially Linear Estimators.
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.
Useful links