A method for automating token causal explanation and discovery

Explaining why events occur is key to making decisions, assigning blame, and enacting policies. Despite the need, few methods can compute explanations in an automated way. Existing solutions start with a type-level model (e.g. factors affecting risk of disease), and use this to explain token-level events (e.g. cause of an individual's illness). This is limiting, since an individual's illness may be due to a previously unknown drug interaction. We propose a hybrid method for token explanation that uses known type-level models while also discovering potentially novel explanations. On simulated data with ground truth, the approach finds accurate explanations when observations match what is known, and correctly finds novel relationships when they do not. On real world data, our approach finds explanations consistent with intuition.