A Full DAG Score-Based Algorithm for Learning Causal Bayesian Networks with Latent Confounders

TL;DR

This paper presents the first fully score-based algorithm for learning causal Bayesian networks that can identify latent confounders from observational data, especially for discrete variables.

Contribution

It introduces a novel score-based structure learning algorithm capable of detecting latent confounders in discrete CBNs, filling a significant gap in existing methods.

Findings

Algorithm effectively detects latent confounders in experiments.

Mathematically justified approach shows robustness.

Outperforms existing methods in identifying hidden variables.

Abstract

Causal Bayesian networks (CBN) are popular graphical probabilistic models that encode causal relations among variables. Learning their graphical structure from observational data has received a lot of attention in the literature. When there exists no latent (unobserved) confounder, i.e., no unobserved direct common cause of some observed variables, learning algorithms can be divided essentially into two classes: constraint-based and score-based approaches. The latter are often thought to be more robust than the former and to produce better results. However, to the best of our knowledge, when variables are discrete, no score-based algorithm is capable of dealing with latent confounders. This paper introduces the first fully score-based structure learning algorithm searching the space of DAGs (directed acyclic graphs) that is capable of identifying the presence of some latent confounders. It is justified mathematically and experiments highlight its effectiveness.