Articles
Background
- Neyman Orthogonality in Linear Regression
From Frisch-Waugh-Lovell to DML: understanding why score choice determines valid inference.
Key Features
- Get Started
A brief introduction to Double/Debiased Machine Learning using (short-)stacking in R.
- Computational Benefits of Short-Stacking
Comparison of computational time between short-stacking and traditional stacking.
- Stacking Diagnostics and Cross-Validation Criteria
How to evaluate base learners, interpret ensemble weights, and perform statistical inference on learner performance.
- Robust Inference and Repeated Resampling
- Integration with modelsummary and broom
- Constructing a User-Provided Base Learner
Tutorial on writing a simple wrapper for new user-provided base learner.
- Estimation with Sparse Matrices
Illustration of sparse matrix support.
- Diff-in-Diff Estimation and Aggregation
Tutorial on DiD estimation with ddml_attgt, lincom aggregation, and uniform inference.