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Plenary 2: The Mathematics of Causal Inference: with Reflections on Machine Learning

Speaker  Judea Pearl

Affiliation  UCLA

Host  Evelyne Viegas

Duration  01:11:08

Date recorded  23 April 2013

The development of graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Moreover, problems that were thought to be purely statistical, are beginning to benefit from analyzing their causal roots.

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> Plenary 2: The Mathematics of Causal Inference: with Reflections on Machine Learning