Notes on Forr'e's Notion of Conditional Independence and Causal Calculus for Continuous Variables
Forr'e introduced transitional conditional independence, a unified framework for random and non-stochastic variables in continuous settings. It extends causal calculus using Markov kernels and graphical separation criteria to establish identification rules for interventional kernels, overcoming limitations of classical approaches.