Counterfactual explanations provide individuals with cost-optimal recommendations to achieve their desired outcomes. However, when a significant number of individuals seek similar state modifications, this individual-centric approach can inadvertently create competition and introduce unforeseen costs. Additionally, disregarding the underlying data distribution may lead to recommendations that individuals perceive as unusual or impractical. To address these challenges, we propose a novel framework that extends standard counterfactual explanations by incorporating a population dynamics model. This framework penalizes deviations from equilibrium after individuals follow the recommendations, effectively mitigating externalities caused by correlated changes across the population. By balancing individual modification costs with their impact on others, our method ensures more equitable and efficient outcomes. We show how this approach reframes the counterfactual explanation problem from an individual-centric task to a collective optimization problem. Augmenting our theoretical insights, we design and implement scalable algorithms for computing collective counterfactuals, showcasing their effectiveness and advantages over existing recourse methods, particularly in aligning with collective objectives.
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）；University of Southern California（南加州大学）；Halicioğlu Data Science Institute, University of California San Diego（加利福尼亚大学圣地亚哥分校哈利西奥卢数据科学研究所）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE（教育部知识驱动人机智能工程研究中心）
Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）
Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization
TECNALIA, Basque Research and Technology Alliance (BRTA)（TECNALIA，巴斯克研究与技术联盟）；University of the Basque Country (UPV/EHU)（巴斯克大学）；University of Granada（格拉纳达大学）；DaSCI Andalusian Institute of Data Science and Computational Intelligence（DaSCI安达卢西亚数据科学与计算智能研究所）；Medical University of Graz（格拉茨医科大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；University of Alberta（阿尔伯塔大学）
Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
Department of Economics, Northeastern University（东北大学经济学系）；Center for Health Policy and Healthcare Research, Northeastern University（东北大学健康政策与医疗研究中心）；Department of Public Health and Health Sciences, Bouvé College of Health Sciences, Northeastern University（东北大学布维健康科学学院公共卫生与健康科学系）
GLANCE: Global Actions in a Nutshell for Counterfactual Explainability
Institute for the Management of Information Systems, Athena Research Center（信息管理系统研究所，雅典研究中心）；Department of Electrical and Computer Engineering, National Technical University of Athens（电气与计算机工程系，雅典技术大学）；Max Planck Institute for Software Systems（软件系统马克斯·普朗克研究所）；Université Libre de Bruxelles（布鲁塞尔自由大学）；FARI Institute, Belgium（比利时FARI研究所）；Department of Informatics, Athens University of Economics and Business（信息系，雅典经济与商业大学）；Department of Informatics and Telecommunications, National and Kapodistrian University of Athens（信息与电信系，雅典国家与卡波迪斯蒂亚大学）；Archimedes, Athena Research Center（阿基米德，雅典研究中心）
