liuff19/ReScore
[ICLR 2023] ReScore: Boosting Causal Discovery via Adaptive Sample Reweighting
This tool helps researchers and data scientists improve the accuracy of causal discovery when analyzing complex datasets. It takes your observational data and a preliminary causal graph, then re-weights your data to pinpoint more reliable causal relationships. This is ideal for anyone trying to understand 'why' things happen from their data, rather than just 'what' happens.
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Use this if you are working with observational data and want to uncover more robust causal links between variables, especially when dealing with noisy or complex datasets.
Not ideal if you primarily need to predict outcomes without focusing on the underlying causal mechanisms, or if you don't have an initial idea of potential causal relationships.
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Language
Python
License
MIT
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Last pushed
Mar 11, 2023
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