PacktPublishing/Causal-Inference-and-Discovery-in-Python resource

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

The official code repository for the Packt book 'Causal Inference and Discovery in Python', containing Jupyter Notebook exercises and examples. It teaches Pearlian causal concepts, causal effect estimation, and causal discovery using libraries like DoWhy, EconML, and PyTorch.

Use cases

• learn causal inference in python
• estimate causal effects with dowhy and econml
• discover causal graphs from data
• understand structural causal models and counterfactuals
• work through book exercises on causal machine learning
• apply causal discovery algorithms to a dataset

When to choose

• you are reading the book and want runnable code for each chapter
• you want hands-on Python notebooks covering causal inference from basics to advanced methods
• you want practical examples using DoWhy, EconML, and PyTorch for causal analysis

When to avoid

• you need a production-ready causal inference library rather than educational code
• you want a maintained software package with an API instead of book companion notebooks
• you need causal inference tooling in a language other than Python

Facets

learning-resource · maturity active

machine-learning data-science nlp machine-learning data-science artificial-intelligence tutorials python jvm causal-inference causal-discovery book-code jupyter-notebooks dowhy econml pytorch pearl-causality counterfactuals structural-causal-models packt

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