DoWhy tutorial series

A hands-on tutorial series for causal graphs, identification, estimation, refutation, GCM workflows, and reporting with DoWhy.

DoWhy is the tutorial track for explicit causal assumptions: model the graph, identify the estimand, estimate the effect, and refute or stress-test the result.

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. DoWhy Tutorial 00: Environment And Library Tour
2. DoWhy Tutorial 01: Core Workflow, From Question To Refutation
3. DoWhy Tutorial 02: Causal Graphs, DAGs, And Assumptions
4. DoWhy Tutorial 03: Backdoor Adjustment And Confounding
5. DoWhy Tutorial 04: Regression, Matching, And Propensity Estimators
6. DoWhy Tutorial 05: Weighting, Overlap, And Common Support
7. DoWhy Tutorial 06: Frontdoor, IV, And Natural Experiments
8. DoWhy Tutorial 07: CATE And Heterogeneous Effects
9. DoWhy Tutorial 08: Refuters, Placebos, Negative Controls, And Sensitivity
10. DoWhy Tutorial 09: Graph Discovery And Graph Refutation
11. DoWhy Tutorial 10: GCM Structural Causal Models
12. DoWhy Tutorial 11: Interventions And Counterfactuals With GCM
13. DoWhy Tutorial 12: Mediation, Direct, And Indirect Effects
14. DoWhy Tutorial 13: Root Cause, Anomaly, And Distribution Change
15. DoWhy Tutorial 14: End-To-End Observational Case Study
16. DoWhy 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.