Bayesian network structure learning with causal effects in the presence of latent variables
This addresses the challenge of causal insufficiency in Bayesian networks for researchers in causal inference, though it is incremental as it builds on existing methods like cFCI and hill-climbing.
The paper tackles the problem of learning Bayesian network structures with latent variables, which can cause spurious causal relationships, by proposing a hybrid algorithm called CCHM that combines constraint-based and score-based methods with causal effect measurement. The result is an improvement in reconstructing the true ancestral graph, as demonstrated in experiments on randomized and well-known networks.
Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ancestral relationships. This paper describes a hybrid structure learning algorithm, called CCHM, which combines the constraint-based part of cFCI with hill-climbing score-based learning. The score-based process incorporates Pearl s do-calculus to measure causal effects and orientate edges that would otherwise remain undirected, under the assumption the BN is a linear Structure Equation Model where data follow a multivariate Gaussian distribution. Experiments based on both randomised and well-known networks show that CCHM improves the state-of-the-art in terms of reconstructing the true ancestral graph.