  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.
