[Darmani]

> A non-causal ML approach would seek to determine whether a patient has a disease based on some symptoms, and then send them to a doctor based on those results, sidestepping the need for causal models.

I saw an ML presentation a few months ago, on training a decision tree to do the same thing as a neural net, so we can understand what the neural net was doing.

They used this on a neural net trying to diagnose people with diabetes. It showed that having any other diagnosis would increase its probability of diagnosing them with diabetes. Why? Because it meant they're more likely to have gone to the doctor to get diagnosed. (Along with detecting general health indicators that weren't screened out.)

You can try to partition your data into intervention/non-intervention, or do something else to try to stop your model from detecting spurious correlations. Causal models makes this more formal and tells you which things you should include/exclude, gives you formulas for adjusting them out, and how much bias you introduce by failing to do so.

The theory of causal inference is also immune to distributional shift, and serves as a nice guidance for what actual systems should do (usually: failing to return an answer).

(Yes, I've fully drunk the Pearl Kool-Ade.)

[Eridrus]

Thanks for the example, it does motivate it a bit better when there are more complicated (but still relatively simple) causal relationships.