EconML tutorial series

A hands-on tutorial series for heterogeneous treatment effects, orthogonal learners, causal forests, meta-learners, policy learning, and CATE reporting with EconML.

EconML is the tutorial track for CATE estimation and treatment targeting once the causal question and adjustment strategy are clear.

Notebook links open rendered HTML pages generated from the source notebooks under notebooks/tutorials/. Rendering is configured not to execute notebooks during site builds, so the pages are safe to publish even when a notebook contains heavier optional cells.

Notebook Sequence

1. EconML Tutorial 00: Environment And Library Tour
2. EconML Tutorial 01: CATE Foundations And Potential Outcomes
3. EconML Tutorial 02: Double Machine Learning Basics
4. EconML Tutorial 03: LinearDML And SparseLinearDML
5. EconML Tutorial 04: CausalForestDML
6. EconML Tutorial 05: DRLearner And Doubly Robust Estimation
7. EconML Tutorial 06: Meta-Learners: S, T, And X Learners
8. EconML Tutorial 07: Policy Learning And Treatment Targeting
9. EconML Tutorial 08: Interpretability, SHAP, And Segments
10. EconML Tutorial 09: Inference, Intervals, And Uncertainty
11. EconML Tutorial 10: Multiple Treatments And Continuous Treatments
12. EconML Tutorial 11: Instrumental Variables With DMLIV, OrthoIV, And DeepIV Concepts
13. EconML Tutorial 12: Panel And Longitudinal Extensions
14. EconML Tutorial 13: Estimator Comparison Benchmark
15. EconML Tutorial 14: End-To-End Case Study
16. EconML Tutorial 15: Common Pitfalls, Debugging, And Reporting

How To Use This Tutorial Series

• Start with the environment and library-tour notebook.
• Continue in order if you want a coherent package course.
• Jump to individual notebooks when you need a specific estimator, diagnostic, or reporting pattern.
• Keep the causal design separate from the package API: the library helps implement the workflow, but the assumptions still need to be stated and defended.