#37 Causality and potential outcomes with Irineo Cabreros - a podcast by Roman Cheplyaka
from 2019-09-27T19:00
::
::
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.
Further episodes of the bioinformatics chat
Further podcasts by Roman Cheplyaka
Website of Roman Cheplyaka