akelleh/causality

Health v2 · maintenance only

Adoption not part of the score

1081 stars · 131 forks observed · 2026-08-28

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

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

library · maturity maintenance

data-science machine-learning data-visualization data-science machine-learning analytics python causal-inference causal-graphs dag propensity-score-matching pandas observational-data statistics

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