论文标题
图像数据的珍珠因果层次结构：复杂性和挑战
Pearl Causal Hierarchy on Image Data: Intricacies & Challenges
论文作者
论文摘要
许多研究人员对Pearl的反事实关系理论表示支持，这是AI/ML Research智能系统最终目标的垫脚石。就像在任何其他不断增长的子领域一样，耐心似乎是一种美德，因为从两个领域整合概念的重大进展需要时间，但是，诸如缺乏地面真相基准或对经典问题（例如计算机视觉）的统一观点之类的主要挑战似乎阻碍了研究运动的势头。这项目前的工作说明了如何通过提供几种复杂性的见解来理解珍珠因果层次结构（PCH），但在将珍珠因果关系应用于图像数据研究的关键概念时，自然会出现的挑战。
Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems. As in any other growing subfield, patience seems to be a virtue since significant progress on integrating notions from both fields takes time, yet, major challenges such as the lack of ground truth benchmarks or a unified perspective on classical problems such as computer vision seem to hinder the momentum of the research movement. This present work exemplifies how the Pearl Causal Hierarchy (PCH) can be understood on image data by providing insights on several intricacies but also challenges that naturally arise when applying key concepts from Pearlian causality to the study of image data.
