mingzhang-yin/CoCo
An optimization-based algorithm to accurately estimate the causal effects and robustly predict under distribution shifts. It leverages the invariance of causality over multiple environments.
This project helps researchers and data scientists uncover the true cause-and-effect relationships within their data, rather than just correlations. It takes in datasets collected from different conditions or 'environments' where the underlying causal links are stable, and it outputs a model that accurately estimates these causal effects. This is for anyone who needs to understand 'why' something happens, not just 'what' will happen.
No commits in the last 6 months.
Use this if you need to determine the actual causal drivers behind an outcome and have access to data collected under varying conditions or environments.
Not ideal if your primary goal is simply to predict an outcome with the highest possible accuracy, without needing to understand the underlying causal mechanisms.
Stars
14
Forks
3
Language
Python
License
MIT
Category
Last pushed
Jul 10, 2024
Commits (30d)
0
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