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[D] What are people working on when they say they work on Causal ML? (alien.top)

submitted 1 year ago by poitrenaud@alien.top to c/machinelearning@academy.garden

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Genuinely trying to understand.

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[–] DigThatData@alien.top 1 points 1 year ago (2 children)

causal inference, leveraging machine learning methods to either estimate causal effects, infer causal graphs, or both. if you're not familiar with "causal inference", start with learning about that first and then move up to ml applications within that domain after getting your feet wet with CI first. Judea Pearl's "The Book of Why" is a good introduction to the topic.

[–] poitrenaud@alien.top 1 points 1 year ago (1 children)

Thank you! I’m comfortable with causal inference and have read “The Book of Why”. What do you recommend for the next step?

[–] TwistedBrother@alien.top 1 points 1 year ago

One place might be to look at Granger Causality. I believe that causal ML can look for patterns in data that appear to conform to granger causality structures (ie there’s a leading and lagging indicator, if one always tracks the other then we can start to consider causality).

Normally causality is established in an experiment or natural experiment where we can isolate factors but since we have so much transactional data we can start to see patterns that resemble these structures without delineating the natural experiment ahead of time.

But causality is often very hard outside of very careful structures and it’s still a very active area.

One related place to look is also at network models like SAOMs which use panel data to explore issues with selection versus influence.