Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
Research Centre for Information Systems Engineering (LIRIS)（信息系统工程研究中心（LIRIS））；KU Leuven（鲁汶大学）；School of Information Systems（信息系统学院）；Queensland University of Technology（昆士兰科技大学）；Human Technology Institute（人类技术研究所）；University of Technology Sydney（悉尼科技大学）
Alexander Stevens, Chun Ouyang, Johannes De Smedt, Catarina Moreira
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.
Evaluating Counterfactual Explanation Methods on Incomplete Inputs
Department of Computing, Imperial College London（伦敦帝国理工学院计算系）；TU Dortmund University（多特蒙德工业大学）；Center for Trustworthy Data Science and Security, University Alliance Ruhr（鲁尔大学联盟可信数据科学与安全中心）
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting
Counterfactual Reasoning with Knowledge Graph Embeddings
Faculty of Computer Science, University of Vienna（维也纳大学计算机学院）；UniVie Doctoral School Computer Science, University of Vienna（维也纳大学UniVie计算机博士学院）；Faculty of Philological and Cultural Studies, University of Vienna（维也纳大学语文学与文化学院）
Provably Robust Bayesian Counterfactual Explanations under Model Changes
School of Computing and Digital Technologies（计算与数字技术学院）；Sheffield Hallam University（谢菲尔德哈姆大学）；Lee Kong Chian School of Medicine（李科金医学院）；Nanyang Technological University（南洋理工大学）
IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；TU Graz（格拉茨工业大学）；BOKU University Vienna（维也纳农业大学）；TECNALIA（泰克纳利亚）；University of the Basque Country (EHU)（巴斯克大学）
AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions
School of Computing and Digital Technologies, Sheffield Hallam University（谢菲尔德哈勒姆大学计算与数字技术学院）；Centre of Excellence in AI and Robotics (CEAIR), Sheffield Hallam University（谢菲尔德哈勒姆大学人工智能与机器人卓越中心）；Laboratoire d’analyse cognitive de l’information (LANCI), Université du Québec à Montréal（魁北克大学蒙特利尔分校信息认知分析实验室）；Institut Santé et société (ISS), Université du Québec à Montréal（魁北克大学蒙特利尔分校健康与社会研究所）；Institut de recherches et d’études féministes (IREF), Université du Québec à Montréal（魁北克大学蒙特利尔分校女权主义研究与教育研究所）
Counterfactual Explanations for Deep Two-Sample Testing
Hasso-Plattner-Institute, University of Potsdam（波茨坦大学洪堡-劳恩堡研究所）；Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai（辛辛那提医学院洪堡数字健康研究所）
