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Where can causal inference be used with machine learning in a data science project lifecycle?
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Causal inference can be used after the modeling phase in order to confirm some correlations between variables and the target/outcome.

For example, if the model has good accuracy and gives you a high correlation/association between input $A$ and the target $B$ you may want to perform a causal inference to validate that $A$ has an effect on $B$. But then I ponder whether that’s all Causality can help me achieve or is there some other application of Causality in Machine learning.

Source: https://towardsdatascience.com/causality-in-machine-learning-101-for-dummies-like-me-f7f161e7383e
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