University of Pittsburgh

An Instance-Specific Algorithm for Learning the Structure of Causal Bayesian Networks Containing Latent Variables

PhD Candidate
Friday, February 21, 2020 - 1:00pm - 1:30pm

Abstract: Almost all of the algorithms for learning causal Bayesian networks (CBNs) from observational data assume that the instances in the population share the same causal structure. While accurately learning such population-wide CBN models is useful, learning CBNs that are specific to each instance is often important as well. For example, a breast cancer tumor in a patient (instance) is often a composite of causal mechanisms, where each of these individual causal mechanisms may appear relatively frequently in breast-cancer tumors of other patients, but the particular combination of mechanisms is unique to the current tumor. Therefore, it is critical to discover the specific set of causal mechanisms that are operating in each patient to understand and treat that particular patient effectively. We introduce a novel instance-specific causal structure learning algorithm that uses partial ancestral graphs (PAGs) to model latent confounders. Simulations support that the proposed instance-specific method can improve structure-discovery performance compared to an existing PAG-learning method called GFCI, which is not instance-specific. We also report results that provide support for instance-specific causal relationships existing in real-world datasets.

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