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

TL;DR

This paper introduces a comprehensive SAT-based method for discovering cyclic causal models with latent variables from observational and experimental data, capable of integrating background knowledge and handling complex causal structures.

Contribution

It presents a general, complete procedure that can identify causal edges in models with feedback loops and hidden variables, extending existing algorithms.

Findings

The method effectively discovers causal structures in simulated data.

It scales well with increased data complexity.

The approach generalizes many existing constraint-based algorithms.

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.