  In recent years, various machine and deep learning architectures have been
successfully introduced to the field of predictive process analytics.
Nevertheless, the inherent opacity of these algorithms poses a significant
challenge for human decision-makers, hindering their ability to understand the
reasoning behind the predictions. This growing concern has sparked the
introduction of counterfactual explanations, designed as human-understandable
what if scenarios, to provide clearer insights into the decision-making process
behind undesirable predictions. The generation of counterfactual explanations,
however, encounters specific challenges when dealing with the sequential nature
of the (business) process cases typically used in predictive process analytics.
Our paper tackles this challenge by introducing a data-driven approach,
REVISEDplus, to generate more feasible and plausible counterfactual
explanations. First, we restrict the counterfactual algorithm to generate
counterfactuals that lie within a high-density region of the process data,
ensuring that the proposed counterfactuals are realistic and feasible within
the observed process data distribution. Additionally, we ensure plausibility by
learning sequential patterns between the activities in the process cases,
utilising Declare language templates. Finally, we evaluate the properties that
define the validity of counterfactuals.
