Transitional Conditional Independence
The episode discusses Patrick Forré's paper, "Transitional Conditional Independence," which introduces a new framework for conditional independence when variables are not random. Hosts explore its core factorization, asymmetry, and implications for machine learning and causal inference. The paper provides a new language to handle parameters and treatments without treating them as random.