Knowledge transfer for learning subject-specific causal models

Subject-specific causal models are appropriate for domains such as biology, medicine, and neuroscience, where the causal relations vary across the individuals of a population. However, its learning could be challenging, particularly under limited data sets. Although some works have addressed this issue, they are restricted to discovering up to Markov equivalence classes. In this work, we propose a method for the causal relations identification of subject-specific models. We hypothesized that transferring related data sets and locally performing interventions improves the causal direction identification of relations. The experimental results on true and imperfect Markov equivalence classes of synthetic causal Bayesian networks show that our method performing interventions over several subsets of the candidate parents and using related data according to their differences with target, recovers a higher number of correct oriented edges.