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

Causal Inference and Discovery in Python by Packt Publishing

Repository: https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python
Canonical: https://ross.abutalabs.com/products/causal-inference-and-discovery-in-python
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-07-15T07:16:08+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 92, release rhythm 35, longevity 100
- inputs: {"age_days": 1591, "days_push": 49, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1029, forks 358 (observed 2026-08-28T04:03:17.714647+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, nlp
- domain: machine-learning, data-science, artificial-intelligence, tutorials
- platform: python, jvm
- tags: causal-inference, causal-discovery, book-code, jupyter-notebooks, dowhy, econml, pytorch, pearl-causality, counterfactuals, structural-causal-models, packt

## Member repositories
- PacktPublishing/Causal-Inference-and-Discovery-in-Python (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:17.714647+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:07:37.540941+00:00, confidence not recorded.
  - readme: https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python (fetched 2026-08-28T04:03:17.714647+00:00, sha ebe179d1909b)
- Data as of 2026-08-30T08:39:29.467469+00:00.