ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus (2018)

Re:ML beyond Curve Fitting: An Intro to Causal Inference and... by notslow on Tuesday January 05, 2021 @08:55AM (#25645503)

Causal inference is only helpful is fields where an intervention is needed (like social science, medicine, etc.), which limits its applicability somewhat. Sure, ML is only correlation, but for most problems correlation is all you need.

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Re:ML beyond Curve Fitting: An Intro to Causal Inference and... by malshe on Tuesday January 05, 2021 @11:34AM (#25647202)

It's quite useful in business functions such as marketing

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Re:ML beyond Curve Fitting: An Intro to Causal Inference and... by harperlee on Tuesday January 05, 2021 @12:11PM (#25647747)

I think that in reality it is helpful in most situations, as you are most likely doing the analysis to decide on actions to do, which are interventions and thus assume a causal graph. It’s just that, normally, the impact of misidentifying a correlation as a cause is cheap, and corrected soon enough, so even if it is helpful, it is not essential, and it might not even justify the extra effort.

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Re:ML beyond Curve Fitting: An Intro to Causal Inference and... by melenaboija on Tuesday January 05, 2021 @09:00AM (#25645553)

Any ML model that affect people can be analyzed from a social science point of view. Maybe causal inference could be used for model fairness/bias

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