Causal discovery with functional causal models: Different types of "independence"

Title: 

Causal discovery with functional causal models: Different types of "independence"

 

Abstract: 

Recently a class of causal discovery methods based on functional causal models has been proposed, which, under certain conditions, is able to fully identify the causal structure. Generally speaking, those methods make use of additional properties of a causal system other than conditional independence relationships. In this talk I will talk about three types of "independence" in the functional causal models that help tell cause from effect. They are 1) statistical independence between the cause and noise, 2) independence between the distribution of the cause and the transformation from the cause to the effect, and 3) independence between the parameters in the generating process of the cause and those that generate the effect from the cause. I will illustrate their differences, and compare functional causal model based causal discovery approaches again constraint-based ones.

 

Bio: 

Kun will be in Kumpula all day Tuesday.  Please contact Patrik Hoyer ([email protected]) or Kun directly ([email protected]) if you would like to meet with him during his visit.

 

Kun Zhang is currently a senior research scientist at Max-Planck Institute for Intelligent Systems, Dept. Empirical Inference. His research interests include causal discovery, statistical machine learning (especially from a causal perspective), computational finance, and neuroscience.