Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
MIC Lab, College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院MIC实验室）；Key Laboratory of Trustworthy Distributed Computing and Service (MoE), Beijing University of Posts and Telecommunications（北京邮电大学可信分布式计算与服务重点实验室）；Institute of Artificial Intelligence, Beihang University and Zhongguancun Laboratory（北航人工智能研究院和中关村实验室）
Inferring user preferences from the historical feedback of users is a valuable problem in recommender systems. Conventional approaches often rely on the assumption that user preferences in the feedback data are equivalent to the real user preferences without additional noise, which simplifies the problem modeling. However, there are various confounders during user-item interactions, such as weather and even the recommendation system itself. Therefore, neglecting the influence of confounders will result in inaccurate user preferences and suboptimal performance of the model. Furthermore, the unobservability of confounders poses a challenge in further addressing the problem. To address these issues, we refine the problem and propose a more rational solution. Specifically, we consider the influence of confounders, disentangle them from user preferences in the latent space, and employ causal graphs to model their interdependencies without specific labels. By cleverly combining local and global causal graphs, we capture the user-specificity of confounders on user preferences. We theoretically demonstrate the identifiability of the obtained causal graph. Finally, we propose our model based on Variational Autoencoders, named Causal Structure representation learning of Confounders in latent space (CSC). We conducted extensive experiments on one synthetic dataset and five real-world datasets, demonstrating the superiority of our model. Furthermore, we demonstrate that the learned causal representations of confounders are controllable, potentially offering users fine-grained control over the objectives of their recommendation lists with the learned causal graphs.
Northwestern Polytechnical University（西北工业大学）；Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences（中国科学院分子细胞科学卓越创新中心）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；South China University of Technology（华南理工大学）；Guangdong Institute of Intelligence Science and Technology（广东智能科学技术研究院）；Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）
School of Mathematics, Physics, and Computing, University of Southern Queensland（数学、物理与计算学院，南方昆士兰大学）；School of Data Science, Lingnan University（数据科学学院，岭南大学）；School of Business, University of Southern Queensland（商学院，南方昆士兰大学）；Schulich School of Engineering, University of Calgary（Schulich工程学院，卡尔加里大学）
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
College of Informatics, Huazhong Agricultural University（华中农业大学信息学院）；Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO)（联邦科学与工业研究组织数据61分部）；STEM, University of South Australia（南澳大学 STEM 学院）
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
Guangdong University of Technology（广东工业大学）；Peng Cheng Laboratory（鹏城实验室）；Shantou University（汕头大学）；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）
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
Columbia University（哥伦比亚大学）；Netflix Inc.（奈飞公司）；Mila；HEC Montréal（蒙特利尔高等商学院）
推荐的目标是向用户展示他们喜欢的项目。虽然通常被框定为预测，但推荐的精神是回答一个介入性的问题-对于每个用户和电影，如果我们“强迫”用户观看电影，评分会是多少？为此，我们开发了一种因果推荐方法，其中观看电影是一种“治疗”，用户的评级是一种“结果”。“问题是可能存在未观察到的混杂因素，这些变量影响用户观看的电影以及他们如何评价它们;未观察到的混杂因素阻碍了观察数据的因果预测。为了解决这个问题，我们开发了去基推荐器，一种使用经典推荐模型进行因果推荐的方法。在Wang & Blei [23]之后，去基推荐器涉及两个概率模型。第一个模型是用户观看的电影;它为未观察到的混杂因素提供了替代品。第二个模型模拟每个用户如何评价每部电影;它使用替代品来帮助解释混淆因素。这种两阶段方法消除了由于混淆而导致的偏倚。它改善了推荐，并在测试集上具有稳定的性能。
Causal Effect Identification in LiNGAM Models with Latent Confounders
Technical University of Munich（慕尼黑工业大学）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Leiden University（莱顿大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Science and Technology of China（中国科学技术大学）；Beijing Institute of Technology（北京理工大学）
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）；Department of Philosophy, Carnegie Mellon University（卡内基梅隆大学哲学系）；Pazhou Lab（琶洲实验室）；School of Mathematics and Big Data, Foshan University（佛山大学数学与大数据学院）
Estimating causal effects of continuous-time dynamic treatments with unmeasured confounders
Key Laboratory of Advanced Theory and Application in Statistics and Data Science -MOE, School of Statistics, East China Normal University（华东师范大学统计学院统计学与数据科学前沿理论及应用教育部重点实验室）
Causal Effect Inference with Deep Latent-Variable Models
University of Amsterdam（阿姆斯特丹大学）；TNO Intelligent Imaging（荷兰应用科学研究组织智能成像）；New York University（纽约大学）；CIMS（库朗数学科学研究所）；Massachusetts Institute of Technology（麻省理工学院）；CSAIL & IMES（计算机科学与人工智能实验室与医学工程与科学研究所）；University of Toronto（多伦多大学）；CIFAR（加拿大高等研究院）
