Causality and potential outcomes with Irineo Cabreros — the bioinformatics chat, a podcast about bioinformatics

Causality and potential outcomes with Irineo Cabreros (#37)

September 27, 2019

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).

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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)

Music: Eric Skiff — Come and Find Me (modified, licensed under CC BY 4.0).

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