User guide#
- 1. The basics of double/debiased machine learning
- 2. The data-backend DoubleMLData
- 3. Models
- 4. Score functions
- 5. Double machine learning algorithms
- 6. Learners, hyperparameters and hyperparameter tuning
- 7. Variance estimation and confidence intervals for a causal parameter of interest
- 8. Confidence bands and multiplier bootstrap for valid simultaneous inference
- 9. Sample-splitting, cross-fitting and repeated cross-fitting