py-why/dowhy

Health v2 · maintenance only

Adoption not part of the score

8335 stars · 1057 forks observed · 2026-10-04

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. It unifies causal graphical models and potential outcomes frameworks to estimate causal effects and perform root-cause analysis.

Use cases

• estimate the causal effect of a treatment on an outcome
• test causal assumptions before estimating effects
• perform root-cause analysis of latency spikes in microservices
• analyze causes of customer churn
• measure the impact of a loyalty program on revenue
• do causal attribution for changes in a supply chain

When to choose

• you need principled causal effect estimation with explicit assumption modeling
• you want refutation tests and sensitivity analysis for causal estimates
• you need root-cause analysis using graphical causal models
• you prefer a Python ecosystem library that integrates with pandas and scikit-learn

When to avoid

• you only need predictive machine learning without causal questions
• you need a point-and-click GUI for causal analysis
• your data has no plausible causal structure or assumptions to encode

Facets

library · maturity stable

machine-learning data-science analytics data-science machine-learning python cross-platform causal-inference causal-graphical-models treatment-effects do-calculus bayesian-networks root-cause-analysis statistics

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