Observational studies require adjustment for confounding factors that are correlated with both the treatment and outcome. In the setting where the observed variables are tabular quantities such as average income in a neighborhood, tools have been developed for addressing such confounding. However, in many parts of the developing world, features about local communities may be scarce. In this context, satellite imagery can play an important role, serving as a proxy for the confounding variables otherwise unobserved. In this paper, we study confounder adjustment in this non-tabular setting, where patterns or objects found in satellite images contribute to the confounder bias. Using the evaluation of anti-poverty aid programs in Africa as our running example, we formalize the challenge of performing causal adjustment with such unstructured data -- what conditions are sufficient to identify causal effects, how to perform estimation, and how to quantify the ways in which certain aspects of the unstructured image object are most predictive of the treatment decision. Via simulation, we also explore the sensitivity of satellite image-based observational inference to image resolution and to misspecification of the image-associated confounder. Finally, we apply these tools in estimating the effect of anti-poverty interventions in African communities from satellite imagery.
CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers for Causally Constrained Predictions
Kivira Health（Kivira健康公司）；The Sense Innovation and Research Center, CHUV, Lausanne, Switzerland（The Sense创新与研究中心，CHUV，洛桑，瑞士）；Centre for Vision, Speech and Signal Processing, University of Surrey, U.K.（视觉、语音和信号处理中心，萨里大学，英国）；Institute of Psychology, University of Lausanne, Switzerland（心理学研究所，洛桑大学，瑞士）；EPFL, Switzerland（瑞士联邦理工学院）
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
Data Science and AI Innovation Research Promotion Center, Shiga University（Shiga大学数据科学与人工智能创新研究促进中心）；National Institute of Science and Technology Policy (NISTEP)（国家科学技术政策研究所）；Department of Health Data Science, Tokyo Medical University Graduate School of Medicine（东京医科大学医学研究生院健康数据科学系）；Human Health Sciences, Kyoto University（京都大学人类健康科学）；Graduate School of Medicine Human Health Sciences, Kyoto University（京都大学医学研究生院人类健康科学）；Center for Advanced Intelligence Project, RIKEN（RIKEN高级智能项目）；Department of Epidemiology Infectious Disease Control and Prevention, Hiroshima University（广岛大学流行病学与传染病防控系）；Graduate School of Biomedical and Health Sciences Data Science and AI Innovation Research Promotion Center, Shiga University（Shiga大学生物医学与健康科学研究生院数据科学与人工智能创新研究促进中心）；SANKEN, The University of Osaka Faculty of Data Science, Shiga University（大阪大学数据科学学院，Shiga大学SANKEN）；Institute for the Advanced Study of Human Biology, Kyoto University（京都大学人类生物学高级研究机构）；Department of Physics, Kyoto University（京都大学物理系）；Graduate School of Engineering, The University of Tokyo（东京大学工学研究生院）；Research and Development Center for Large Language Models, National Institute of Informatics（信息处理研究所大型语言模型研究开发中心）
Integrating summarized data from multiple genetic variants in Mendelian randomization: bias and coverage properties of inverse-variance weighted methods
Causal Discovery Should Embrace the Wisdom of the Crowd
Department of Industrial and Systems Engineering, University of Washington（华盛顿大学工业与系统工程系）；Department of Applied Mathematics, Illinois Institute of Technology（伊利诺伊理工学院应用数学系）；Department of Electrical & Computer Engineering, Texas A&M University（德克萨斯阿灵顿大学电气与计算机工程系）
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
Institute of Service Science, National Tsing Hua University（服务科学研究所，国立清华大学）；Faculty of Business and Economics, University of Antwerp（商学院与经济学院，安特卫普大学）；Copenhagen Business School（哥本哈根商学院）
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
Stanford University（斯坦福大学）；University of Toronto（多伦多大学）；Princess Margaret Cancer Centre（玛格丽特公主癌症中心）；Hikvision Research Institute（海康威视研究院）；Chan Zuckerberg Biohub（陈·扎克伯格生物中心）；SickKids Research Institute（病童医院研究所）；Vector Institute（向量研究所）
The State of the Art in Integrating Machine Learning into Visual Analytics
Georgia Tech（佐治亚理工学院）；University of North Carolina Charlotte（北卡罗来纳大学夏洛特分校）；City University of London（伦敦城市大学）；Middlesex University（密德萨斯大学）；Aston University（阿斯顿大学）；University of Oviedo（奥维耶多大学）；Paris 1 Panthéon Sorbonne University（巴黎第一先贤祠-索邦大学）
