CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks

Given the increasing promise of graph neural networks (GNNs) in real-world applications, several methods have been developed for explaining their predictions. Existing methods for interpreting predictions from GNNs have primarily focused on generating subgraphs that are especially relevant for a particular prediction. However, such methods are not counterfactual (CF) in nature: given a prediction, we want to understand how the prediction can be changed in order to achieve an alternative outcome. In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes. Using only edge deletions, we find that our method, CF-GNNExplainer, can generate CF explanations for the majority of instances across three widely used datasets for GNN explanations, while removing less than 3 edges on average, with at least 94\% accuracy. This indicates that CF-GNNExplainer primarily removes edges that are crucial for the original predictions, resulting in minimal CF explanations.Comment: Accepted to AISTATS 202

Patients who do not complete cardiac rehabilitation have an increased risk of cardiovascular events during long-term follow-up

Background: Cardiac rehabilitation (CR) has favourable effects on cardiovascular mortality and morbidity. Therefore, it might reasonable to expect that incomplete CR participation will result in suboptimal patient outcomes. Methods: We studied the 914 post-acute coronary syndrome patients who participated in the OPTImal CArdiac REhabilitation (OPTICARE) trial. They all started a ‘standard’ CR programme, with physical exercises (group sessions) twice a week for 12 weeks. Incomplete CR was defined as participation in <75% of the scheduled exercise sessions. Patients were followed-up for 2.7 years, and the incidence of cardiac events was recorded. Major adverse cardiac events