Explaining a machine learning decision to physicians via counterfactuals
Supriya Nagesh Nina Mishra Yonatan Naamad James M. Rehg Mehul A. Shah Alexei Wagner
Machine learning models perform well on several healthcare tasks and can help
reduce the burden on the healthcare system. However, the lack of explainability
is a major roadblock to their adoption in hospitals. \textit{How can the
decision of an ML model be explained to a physician?} The explanations
considered in this paper are counterfactuals (CFs), hypothetical scenarios that
would have resulted in the opposite outcome. Specifically, time-series CFs are
investigated, inspired by the way physicians converse and reason out decisions
`I would have given the patient a vasopressor if their blood pressure was lower
and falling'. Key properties of CFs that are particularly meaningful in
clinical settings are outlined: physiological plausibility, relevance to the
task and sparse perturbations. Past work on CF generation does not satisfy
these properties, specifically plausibility in that realistic time-series CFs
are not generated. A variational autoencoder (VAE)-based approach is proposed
that captures these desired properties. The method produces CFs that improve on
prior approaches quantitatively (more plausible CFs as evaluated by their
likelihood w.r.t original data distribution, and 100$\times$ faster at
generating CFs) and qualitatively (2$\times$ more plausible and relevant) as
evaluated by three physicians.