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

causality matters, what does and does not imply causality, and twodifferent mathematical formalizations of causality: potential outcomes anddirected acyclic graphs (DAGs). Causal models areusually considered external to and separate from statistical models, whereasIrineo’s new paper shows how causality can be viewed as a relationship betweenparticularly 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)