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Causal Identification: Are We There Yet?

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If you have a question about this talk, please contact Prof. Ramji Venkataramanan.

We discuss causal identifiability, the canonical problem of causal inference, where the goal is to calculate the effect of intervening on subset of variables on an outcome variable of interest. We first visit the definition of the problem and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem as without such an assumption the rules of do-calculus and consequently the proposed algorithms in the field are not sound. After discussing state of the art and recent progress in the field, we present some of the open problems and remaining challenges.

This talk is part of the Information Theory Seminar series.

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