Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure

Title

Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure

 

Abstract

We present a very general approach to learning the structure of causal models based on d-separation constraints, obtained from any given set of overlapping passive observational or experimental data sets. The procedure allows for both directed cycles (feedback loops) and the presence of latent variables. Our approach is based on a logical representation of causal pathways, which permits the integration of quite general background knowledge, and inference is performed using a Boolean satisfiability (SAT) solver. The procedure is complete in that it exhausts the available information on whether any given edge can be determined to be present or absent, and returns ``unknown'' otherwise. Many existing constraint-based causal discovery algorithms can be seen as special cases, tailored to circumstances in which one or more restricting assumptions apply. Simulations illustrate the effect of these assumptions on discovery and how the present algorithm scales.

 

About the presenter

Dr. Antti Hyttinen is a researcher at the Department of Computer Science, University of Helsinki. He received his M.Sc. degree from Tampere University of Technology in 2004. He received his Ph.D. in June 2013, working in the Neuroinformatics research group at the department. His research interests include machine learning, probabilistic models and causality.