In this episode, I talk with Irineo Cabreros about causality. We discuss why

causality matters, what does and does not imply causality, and two

different mathematical formalizations of causality: potential outcomes and

directed acyclic graphs (DAGs). Causal models are

usually considered external to and separate from statistical models, whereas

Irineo’s new paper shows how causality can be viewed as a relationship between

particularly chosen random variables (potential outcomes).

Links:

Causal models on probability spaces (Irineo Cabreros, John D. Storey)

The Book of Why: The New Science of Cause and Effect (Judea Pearl, Dana Mackenzie)

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