the bioinformatics chat

#37 Causality and potential outcomes with Irineo Cabreros

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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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