Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound
This addresses the challenge of latent confounder discovery and density estimation in causal structure learning, which is incremental as it builds on existing methods with minor improvements.
The paper tackles the problem of discovering and learning the distribution of latent confounders in Bayesian networks, combining variational Bayesian methods, expectation-maximisation, hill-climbing search, and structure learning to propose two strategies that perform well relative to existing solutions.
Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we focus on both discovering and learning the distribution of latent confounders. This task requires solutions that come from different areas of statistics and machine learning. We combine elements of variational Bayesian methods, expectation-maximisation, hill-climbing search, and structure learning under the assumption of causal insufficiency. We propose two learning strategies; one that maximises model selection accuracy, and another that improves computational efficiency in exchange for minor reductions in accuracy. The former strategy is suitable for small networks and the latter for moderate size networks. Both learning strategies perform well relative to existing solutions.