# akelleh/causality

Tools for causal analysis

Repository: https://github.com/akelleh/causality
Canonical: https://ross.abutalabs.com/products/causality
Language: Python
License: MIT
License Family: permissive
Last push: 2025-03-11T23:41:53+00:00

## Health v2 (maintenance only)
Score: 27/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 10, release rhythm 8, longevity 100
- inputs: {"age_days": 4117, "days_push": 540, "days_rel": 540, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1081, forks 131 (observed 2026-08-28T04:03:30.697009+00:00)

## What it is
A Python library providing tools for causal inference and analysis from observational data, built as an extension of pandas DataFrames. It includes causal effect estimation methods like propensity score matching and algorithms for inferring causal DAGs from data.

## Use cases
- estimate causal effects from observational data
- run propensity score matching in python
- infer causal DAG structure from a dataset
- control for confounders when estimating treatment effects
- plot causal effect of a treatment on an outcome
- do causal analysis with pandas dataframes

## When to choose
- you need causal effect estimation or DAG discovery in a pandas-based Python workflow
- you want lightweight causal inference tools without a heavy framework

## When to avoid
- you need modern, actively developed causal inference features like DoWhy or EconML provide
- you only need correlation or standard regression without causal assumptions

## Facets
- artifact type: library
- maturity: maintenance
- function: data-science, machine-learning, data-visualization
- domain: data-science, machine-learning, analytics
- platform: python
- tags: causal-inference, causal-graphs, dag, propensity-score-matching, pandas, observational-data, statistics

## Member repositories
- akelleh/causality (main) score 27

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:30.697009+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 2026-08-30T06:51:19.746389+00:00, confidence not recorded.
  - readme: https://github.com/akelleh/causality (fetched 2026-08-28T04:03:30.697009+00:00, sha 8c4d019adc18)
  - registry_pypi: https://pypi.org/pypi/causality/json (fetched 2026-08-29T12:53:47.037440+00:00, sha 66019b7433a8)
- Data as of 2026-08-30T08:39:29.467469+00:00.