Classical RL isn’t causal, because there’s no confounding (although I think it is very useful to think about classical RL causally, for doing inference more efficiently).
Various extensions of classical RL are causal, of course.
A lot of interesting algorithmic fairness isn’t really causal. Classical prediction problems aren’t causal.
However, I think domain adaptation, covariate shift, semi-supervised learning are all causal problems.
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I think predicting things you have no data on (“what if the AI does something we didn’t foresee”) is sort of an impossible problem via tools in “data science.” You have no data!
I think value learning might be causal because human preferences cannot be observed, and therefore can act as a confounder, similar to the work in
Zhang, J., Kumor, D., Bareinboim, E. Causal Imitation Learning with Unobserved Confounders. In Advances in Neural Information Processing Systems 2020.
At least that was one of my motivations.
I think predicting things you have no data on (“what if the AI does something we didn’t foresee”) is sort of an impossible problem via tools in “data science.” You have no data!
Sure, I agree. I think I was quite inaccurate. I am referring to transportability analysis, to be more specific. This approach should help in new situations where we have not directly trained our system, and in which our preferences could change.
Classical RL isn’t causal, because there’s no confounding (although I think it is very useful to think about classical RL causally, for doing inference more efficiently).
Various extensions of classical RL are causal, of course.
A lot of interesting algorithmic fairness isn’t really causal. Classical prediction problems aren’t causal.
However, I think domain adaptation, covariate shift, semi-supervised learning are all causal problems.
---
I think predicting things you have no data on (“what if the AI does something we didn’t foresee”) is sort of an impossible problem via tools in “data science.” You have no data!
I think value learning might be causal because human preferences cannot be observed, and therefore can act as a confounder, similar to the work in
Zhang, J., Kumor, D., Bareinboim, E. Causal Imitation Learning with Unobserved Confounders. In Advances in Neural Information Processing Systems 2020.
At least that was one of my motivations.
Sure, I agree. I think I was quite inaccurate. I am referring to transportability analysis, to be more specific. This approach should help in new situations where we have not directly trained our system, and in which our preferences could change.