Covariate Dependent Mixture of Bayesian Networks
This addresses the need for personalized interventions in health, education, and social policy by handling data heterogeneity, though it is incremental as it builds on existing mixture models.
The paper tackles the problem of learning Bayesian network structures from heterogeneous data populations by proposing a covariate-dependent mixture model, which identifies network structures and demographic predictors of sub-population membership, as demonstrated in simulations and a youth mental health case study.
Learning the structure of Bayesian networks from data provides insights into underlying processes and the causal relationships that generate the data, but its usefulness depends on the homogeneity of the data population, a condition often violated in real-world applications. In such cases, using a single network structure for inference can be misleading, as it may not capture sub-population differences. To address this, we propose a novel approach of modelling a mixture of Bayesian networks where component probabilities depend on individual characteristics. Our method identifies both network structures and demographic predictors of sub-population membership, aiding personalised interventions. We evaluate our method through simulations and a youth mental health case study, demonstrating its potential to improve tailored interventions in health, education, and social policy.