Deep Learning models have shown success in a large variety of tasks by extracting correlation patterns from high-dimensional data but still struggle when generalizing out of their initial distribution. As causal engines aim to learn mechanisms independent from a data distribution, combining Deep Learning with Causality can have a great impact on the two fields. In this paper, we further motivate this assumption. We perform an extensive overview of the theories and methods for Causality from different perspectives, with an emphasis on Deep Learning and the challenges met by the two domains. We show early attempts to bring the fields together and the possible perspectives for the future. We finish by providing a large variety of applications for techniques from Causality.
A Shift in Perspective on Causality in Domain Generalization
CHAI Hub（CHAI中心）；The University of Edinburgh（爱丁堡大学）；University of Manchester（曼彻斯特大学）；Archimedes, Athena Research Center（阿奇米德与雅典研究所）；King's College London（伦敦国王学院）；University College London（伦敦大学学院）；National and Kapodistrian University of Athens（雅典国家与卡波迪斯蒂亚纳大学）
Emerging Synergies in Causality and Deep Generative Models: A Survey
University of New South Wales（新南威尔士大学）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；CSIRO’s Data61（澳大利亚联邦科学与工业研究组织Data61研究院）
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Bundelkhand Institute of Engineering and Technology（本德尔坎德工程技术学院）；Nanyang Technological University（南洋理工大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (iHARP)（NSF HDR极地地区数据与模型变革研究所）；University of North Texas（北得克萨斯大学）
Tsinghua University（清华大学）；Guangdong University of Technology（广东工业大学）；Peng Cheng Laboratory（鹏城实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Shantou University（汕头大学）
CausalAdv: Adversarial Robustness through the Lens of Causality
University of Science and Technology of China（中国科学技术大学）；Hong Kong Baptist University（香港浸会大学）；The University of Melbourne（墨尔本大学）；The University of Sydney（悉尼大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Monash University（莫纳什大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
On the physics of nested Markov models: a generalized probabilistic theory perspective
QICI Quantum Information and Computation Initiative, School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China（量子信息与计算倡议，计算与数据科学学院，香港大学，香港特别行政区，中国）；Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China（交叉信息科学研究所，清华大学，北京，中国）；Shanghai Qi Zhi Institute, Shanghai, China（上海启智研究所，上海，中国）；Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada（理论物理研究所，滑铁卢，安大略，加拿大）
