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igerber/diff-diff

Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.

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What it does

diff-diff is a Python library that helps researchers and analysts measure the true cause-and-effect impact of a policy, product change, or intervention — for example, whether a new feature actually caused more signups, rather than just coinciding with them. It implements a range of advanced statistical techniques used by economists and data scientists to compare what happened to a treated group versus a control group over time.

Why it matters

For product teams and investors, understanding causation (not just correlation) is the difference between doubling down on something that works and wasting resources on something that looked good by coincidence — making this kind of rigorous analysis a competitive advantage. Having a well-documented, Python-native tool for this lowers the barrier for data teams at startups to run the same quality of impact measurement previously reserved for academic economists or large tech companies.

24Active

On the radar — signal detected

Stars
142
Forks
20
Contributors
2
Language
Python

Score updated Apr 5, 2026

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