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

09/26/2013

∙

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.

READ FULL TEXT

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

A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias

Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables

A Logical Characterization of Constraint-Based Causal Discovery

ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions

Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets

Foundations of Causal Discovery on Groups of Variables

Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models

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

Related Research

A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias

Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables

A Logical Characterization of Constraint-Based Causal Discovery

ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions

Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets

Foundations of Causal Discovery on Groups of Variables

Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models