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#37 Causality and potential outcomes with Irineo Cabreros
Manage episode 243085537 series 1537951
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)
If you enjoyed this episode, please consider supporting the podcast on Patreon.
70 епізодів
Manage episode 243085537 series 1537951
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)
If you enjoyed this episode, please consider supporting the podcast on Patreon.
70 епізодів
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