Tutorials

Tested tutorials for Python and R.

The tutorial suite covers the standard estimator in both Python and R, plus a separate adaptive tutorial. Each tutorial is backed by small synthetic examples and CI smoke tests.

Standard tutorials Adaptive tutorial

Standard Estimator

Standard tutorial track

The standard tutorial track covers raw-data fitting, custom learners, and supplied nuisance matrices in both interfaces.

Custom learners

Built-ins, interface-native learners, and optional backends

Covers built-in model strings, sklearn-compatible estimators in Python, and built-in specs, custom model lists, SuperLearner, and sl3 in R.

Python notebook · Python script · R vignette

Supplied nuisances

Run calibration, debiasing, and inference from matrices

Covers matrix shapes, treatment-level alignment, and when to use the direct nuisance path.

Python notebook · Python script · R vignette

Advanced

Adaptive binary-treatment tutorial

The adaptive estimator has its own tutorial track so the standard and adaptive workflows stay separate.

Adaptive DML

Binary-treatment adaptive workflow

Covers the calibrated R-learner path, the plug-in alternative, and the current binary-treatment scope.

Python notebook · Python script · R vignette

Reference pages

Site pages

Use the site pages for concise workflow guidance, then move to the long-form tutorials when you need runnable end-to-end examples.

Examples · R package · Adaptive