jrfiedler/causal_inference_python_code resource

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1354 stars · 413 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebooks implementing the Python code for Part II of the book 'Causal Inference: What If' by Hernán and Robins. It reproduces the book's Stata, R, and SAS analyses using numpy, pandas, statsmodels, and related libraries.

Use cases

• learn causal inference from the Hernán and Robins book with Python
• replicate book examples like g-formulas and IP weighting in Python
• find Python equivalents of the book's Stata, R, and SAS programs
• study marginal structural models and standardization hands-on
• teach a causal inference course with worked code examples

When to choose

• you are working through 'Causal Inference: What If' and want Python implementations
• you want reference code for causal inference methods like IP weighting and g-computation
• you prefer notebooks that mirror the book's chapter-by-chapter analyses

When to avoid

• you need a production causal inference library with APIs and tests
• you need Part I or Part III of the book, which are not covered
• you require a maintained, licensed package for a software project

Facets

learning-resource · maturity maintenance

data-science machine-learning data-science education tutorials python causal-inference jupyter-notebooks statistics epidemiology book-companion

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