Python Package Tutorials

Applied package tutorial series for causal inference, causal discovery, causal ML, and AI systems workflows.

This section separates complete notebook-backed package tutorials from shorter AI workflow tutorial pages.

Notebook-Backed Causal Python Tutorials

These four tutorial series are built directly from the folders under notebooks/tutorials/.

DoWhy tutorial series

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

May 3, 2026

DoubleML tutorial series

A hands-on tutorial series for double/debiased machine learning, orthogonal scores, cross-fitting, inference, sensitivity analysis, and policy learning with DoubleML.

May 3, 2026

EconML tutorial series

EconML

Causal ML

Heterogeneous Effects

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

May 3, 2026

causal-learn tutorial series

A hands-on tutorial series for causal discovery with PC, FCI, CD-NOD, GES, LiNGAM, functional causal models, permutation methods, stability checks, and reporting.

May 3, 2026

AI Systems Tutorial Pages

These pages cover the broader AI engineering skills that complement the causal inference portfolio: RAG, orchestration, evaluation, tracing, monitoring, and fine-tuning.