Organizers
Lin Liu (刘林), Shanghai Jiao Tong University ✉ 🌐
Zhonghua Liu (刘中华), Columbia University ✉ 🌐
Wenlong Mou (牟文龙), University of Toronto ✉ 🌐
Rajarshi Mukherjee, Harvard University ✉ 🌐
Linbo Wang (王林勃), University of Toronto ✉ 🌐
Yuhao Wang (王禹皓), Tsinghua University ✉ 🌐
Abstract
Causality lies at the heart of scientific discovery and data-driven decision-making. While the philosophical foundations of causality span centuries, recent advances in statistics and machine learning have revolutionized our ability to measure the effects of interventions—from randomized trials to large-scale observational data.
The past two decades have seen tremendous growth in causal analysis methods, powered by interactions between statistical theory, machine learning algorithms, and domain-specific applications. These developments are now influencing fields such as healthcare, economics, social sciences, and artificial intelligence.
This workshop aims to bring together researchers and practitioners in causal analysis as well as related areas in the field of statistics and machine learning to discuss emerging challenges in these fields. This workshop will emphasize both theoretical rigor and practical implementation, fostering dialogue across disciplines to tackle the challenges of causality in complex, data-rich environments.
因果推理是科学发现与数据驱动决策的核心。尽管因果关系的哲学基础已存在数百年，统计学与机器学习的最新进展彻底改变了我们衡量干预效果的能力——从随机实验到大规模观测数据皆然。
过去二十年间，在统计理论、机器学习算法与领域应用的交叉推动下，因果分析方法取得了巨大发展。这些成果正深刻影响着医疗健康、经济学、社会科学与人工智能等领域。
本次研讨会旨在汇聚因果分析及统计学、机器学习相关领域的研究者与实践者，共同探讨这些学科中的新兴挑战。我们将兼顾理论严谨性与实践落地性，通过跨学科对话应对数据密集型复杂环境下的因果推理难题。
