# rguo12/awesome-causality-algorithms

An index of algorithms for learning causality with data

Repository: https://github.com/rguo12/awesome-causality-algorithms
Canonical: https://ross.abutalabs.com/products/awesome-causality-algorithms
License: MIT
License Family: permissive
Topics: causality, causal-inference, causality-analysis, causality-algorithms, unconfoundedness-assumption, baselines, awesome, learning-to-rank, recommender-system, multilabel-classification
Last push: 2025-01-22T11:00:42+00:00

## Health v2 (maintenance only)
Score: 33/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 2, release rhythm 35, longevity 100
- inputs: {"age_days": 2905, "days_push": 588, "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 3279, forks 473 (observed 2026-08-28T04:07:53.554604+00:00)

## What it is
A curated awesome-list index of open-source algorithms and toolboxes for learning causality with data, covering causal inference and causal machine learning. It accompanies an ACM Computing Surveys paper and only lists methods with available code.

## Use cases
- find python libraries for causal inference
- discover causal discovery algorithms with open-source code
- compare toolboxes for treatment effect estimation
- find uplift modeling packages
- research survey of causal machine learning methods
- locate baselines for causal effect estimation

## When to choose
- you need an overview of the causality algorithm landscape
- you are picking a causal inference toolbox for a project
- you want reproducible, code-backed causal methods

## When to avoid
- you need a working library rather than a list of links
- you need methods without open-source implementations

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, developer-tools
- domain: machine-learning, data-science, awesome-lists
- platform: python
- tags: causality, causal-inference, causal-discovery, awesome-list, uplift-modeling, treatment-effect, algorithms

## Member repositories
- rguo12/awesome-causality-algorithms (main) score 33

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:53.554604+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 2026-08-29T18:43:20.924516+00:00, confidence not recorded.
  - readme: https://github.com/rguo12/awesome-causality-algorithms (fetched 2026-08-28T04:07:53.554604+00:00, sha 313667a6e0a6)
- Data as of 2026-08-30T08:39:29.467469+00:00.