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

Understand

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.