  Counterfactual explanations constitute among the most popular methods for
analyzing black-box systems since they can recommend cost-efficient and
actionable changes to the input of a system to obtain the desired system
output. While most of the existing counterfactual methods explain a single
instance, several real-world problems, such as customer satisfaction, require
the identification of a single counterfactual that can satisfy multiple
instances (e.g. customers) simultaneously. To address this limitation, in this
work, we propose a flexible two-stage algorithm for finding groups of instances
and computing cost-efficient multi-instance counterfactual explanations. The
paper presents the algorithm and its performance against popular alternatives
through a comparative evaluation.
