B1766
Title: Non-linear structural causal models with cycles and latent confounders
Authors: Patrick Forre - University of Amsterdam (Netherlands) [presenting]
Joris Mooij - University of Amsterdam (Netherlands)
Abstract: The focus is on a flexible class of general structural causal models that allow for non-/linear functional relations (like neural networks, etc.), arbitrary probability distributions (like discrete, continuous, mixtures, etc.), causal cycles (like feedback, etc.) and latent variables (aka confounders). For such models we will demonstrate several desirable properties, how to do causal reasoning, the rules of do-calculus and graphical criteria for conditional independence relations. We will also show how the latter can be exploited for causal discovery algorithms in such general contexts.
