Machine learning models have undeniably achieved impressive performance across a range of applications. However, their often perceived black-box nature, and lack of transparency in decision-making, have raised concerns about understanding their predictions. To tackle this challenge, researchers have developed methods to provide explanations for machine learning models. In this paper, we introduce LaPLACE-explainer, designed to provide probabilistic cause-and-effect explanations for any classifier operating on tabular data, in a human-understandable manner. The LaPLACE-Explainer component leverages the concept of a Markov blanket to establish statistical boundaries between relevant and non-relevant features automatically. This approach results in the automatic generation of optimal feature subsets, serving as explanations for predictions. Importantly, this eliminates the need to predetermine a fixed number N of top features as explanations, enhancing the flexibility and adaptability of our methodology. Through the incorporation of conditional probabilities, our approach offers probabilistic causal explanations and outperforms LIME and SHAP (well-known model-agnostic explainers) in terms of local accuracy and consistency of explained features. LaPLACE's soundness, consistency, local accuracy, and adaptability are rigorously validated across various classification models. Furthermore, we demonstrate the practical utility of these explanations via experiments with both simulated and real-world datasets. This encompasses addressing trust-related issues, such as evaluating prediction reliability, facilitating model selection, enhancing trustworthiness, and identifying fairness-related concerns within classifiers.
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge
Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；Halicioğlu Data Science Institute (HDSI), University of California San Diego（加州大学圣迭戈分校哈利乔格鲁数据科学研究所）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）
CausalWrap: Model-Agnostic Causal Constraint Wrappers for Tabular Synthetic Data
Department of Biostatistics, Vanderbilt University Medical Center（生物统计学系，范德比尔特大学医学中心）；Department of Biomedical Informatics, Vanderbilt University Medical Center（生物医学信息学系，范德比尔特大学医学中心）
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
Department of Computer Science, Tsinghua University（清华大学计算机科学系）；Bosch Center for Artificial Intelligence（博世人工智能中心）；Computer Science Department, TU Darmstadt（图尔恩大学计算机科学系）
Droplet-LNO: Physics-Informed Laplace Neural Operators for Accurate Prediction of Droplet Spreading Dynamics on Complex Surfaces
Department of Mechanical Engineering, IIT Kharagpur（印度克里希纳加尔帕大学机械工程系）；Department of Computer Science & Engineering, IIT Kharagpur（印度克里希纳加尔帕大学计算机科学与工程系）
Robust Multimodal Representation Learning in Healthcare
University of California, Davis（加州大学戴维斯分校）；HKUST (GZ)（香港科技大学）；University of Rochester（罗切斯特大学）；Tongji University（同济大学）；Georgia Institute of Technology（佐治亚理工学院）；Fudan University（复旦大学）；The University of British Columbia（不列颠哥伦比亚大学）
Institute of Science and Technology Austria (ISTA)（奥地利科学与技术研究所）；Massachusetts Institute of Technology (MIT)（麻省理工学院）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；INRIA, Ecole Normale Supérieure, CNRS, PSL Research University（法国国家信息与自动化研究所、巴黎高等师范学校、法国国家科学研究中心、巴黎-萨克勒大学）
