Certain causal models involving unmeasured variables induce no independence constraints among the observed variables but imply, nevertheless, inequality contraints on the observed distribution. This paper derives a general formula for such instrumental variables, that is, exogenous variables that directly affect some variables but not all. With the help of this formula, it is possible to test whether a model involving instrumental variables may account for the data, or, conversely, whether a given variables can be deemed instrumental.
Disentangled Instrumental Variables for Causal Inference with Networked Observational Data
School of Computer Science and Engineering（计算机科学与工程学院）；Guangxi Key Lab of MSMI（广西 MSMI 重点实验室）；MOE Key Lab of EBIT, Guangxi Normal University（教育部 EBIT 重点实验室，广西师范大学）；School of Computer Science and Technology（计算机科学与技术学院）；University of South Australia（南澳大利亚大学）；School of Information Technology and Mathematical Sciences（信息科技与数学科学学院）；China University of Mining and Technology（中国矿业大学）
IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Jinesis AI Lab（Jinesis AI实验室）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；EuroSafeAI；Carnegie Mellon University（卡内基梅隆大学）；Amazon（亚马逊）
Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA
School of Microelectronics, Tianjin University（天津大学微电子学院）；Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University（江苏大学附属昆山医院骨科部）；Department of Clinical Laboratory, The Third Central Hospital of Tianjin（天津第三中心医院临床实验室）；School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）
