Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
University College London（伦敦大学学院）；University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；ENSAE/CREST（法国国家统计与经济管理学院/经济与统计研究中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
We address the problem of causal effect estimation in the presence of unobserved confounding, but where proxies for the latent confounder(s) are observed. We propose two kernel-based methods for nonlinear causal effect estimation in this setting: (a) a two-stage regression approach, and (b) a maximum moment restriction approach. We focus on the proximal causal learning setting, but our methods can be used to solve a wider class of inverse problems characterised by a Fredholm integral equation. In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting. We provide consistency guarantees for each algorithm, and we demonstrate these approaches achieve competitive results on synthetic data and data simulating a real-world task. In particular, our approach outperforms earlier methods that are not suited to leveraging proxy variables.
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
University of Oxford（牛津大学）；Harvard University（哈佛大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Vector Institute（向量研究所）；University College London（伦敦大学学院）
Instrumental and Proximal Causal Inference with Gaussian Processes
Department of Statistics and Actuarial Science, University of Hong Kong（香港大学统计与精算科学系）；Rational Intelligence Lab, CISPA Helmholtz Center for Information Security（CISPA霍普夫信息安全中心理性智能实验室）；Australian Institute for Machine Learning, Adelaide University（阿德莱德大学澳大利亚机器学习研究所）；College of Computing & Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
Graduate School of AI KAIST（韩国科学技术院人工智能研究生院）；Department of Electrical and Computer Engineering, UBC（不列颠哥伦比亚大学电气与计算机工程系）；Samsung Advanced Institute of Technology（三星先进技术研究院）
Doubly Robust Proximal Causal Learning for Continuous Treatments
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；School of Data Science, Fudan University（复旦大学数据科学学院）；Zhangjiang Fudan International Innovation Center（张江复旦国际创新中心）；Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University)（复旦大学计算神经科学与类脑智能重点实验室）；MOE Frontiers Center for Brain Science, Fudan University（教育部脑科学前沿中心（复旦大学））；Shanghai Engineering Research Center of AI & Robotics, Fudan University（上海市人工智能与机器人工程技术研究中心（复旦大学））；Engineering Research Center of AI & Robotics, Ministry of Education, Fudan University（教育部人工智能与机器人工程研究中心（复旦大学））
Comparing Two Proxy Methods for Causal Identification
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health（约翰霍普金斯大学伯克曼公共卫生学院生物统计学系）；Department of Computer Science, Johns Hopkins Whiting School of Engineering（约翰霍普金斯大学惠廷工程学院计算机科学系）
Closing the problem of which causal structures of up to six total nodes have a classical-quantum gap
Department of Mathematics, University of York（约克大学数学系）；Aix-Marseille University, CNRS, LIS（阿维尼翁-马赛大学，法国国家科学研究中心，LIS）；Department of Mathematics, King's College London（伦敦国王学院数学系）
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Weierstrass Institute for Applied Analysis and Stochastics（魏尔斯特拉斯应用分析与随机过程研究所）；Eidgenössische Technische Hochschule Zürich（苏黎世联邦理工学院）
