the bioinformatics chat

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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