一种以人为本的因果图创建自举方法
A Human-Centered Approach for Bootstrapping Causal Graph Creation
AI总结：
本文提出以人为本的增强现实框架，让人在变量选择、关系建立、干预、反事实解释和图评估中参与因果发现，并以机械臂拾取放置任务验证其潜力。
摘要：
因果推断是经济学、基因组学和医学等学科的基石，如今也日益被视为推动机器人学领域发展的基础。尤其是，从观测数据中推理因果关系的能力，对于机器人系统实现稳健泛化至关重要。然而，构建用于表示因果关系的因果图模型是一项巨大挑战。目前，必须将对因果推断的细致理解以及对因果关系的认识手动编入因果图模型。为解决这一难题，我们展示了面向以人为本的增强现实框架以创建因果图模型的初步成果。具体而言，我们的系统通过让人类在每一步参与选择变量、建立关系、执行干预、生成反事实解释以及评估所得因果图，来自举因果发现过程。我们通过一个执行拾取放置任务的物理机械臂展示了该框架的潜力。
英文摘要：
Causal inference, a cornerstone in disciplines such as economics, genomics, and medicine, is increasingly being recognized as fundamental to advancing the field of robotics. In particular, the ability to reason about cause and effect from observational data is crucial for robust generalization in robotic systems. However, the construction of a causal graphical model, a mechanism for representing causal relations, presents an immense challenge. Currently, a nuanced grasp of causal inference, coupled with an understanding of causal relationships, must be manually programmed into a causal graphical model. To address this difficulty, we present initial results towards a human-centered augmented reality framework for creating causal graphical models. Concretely, our system bootstraps the causal discovery process by involving humans in selecting variables, establishing relationships, performing interventions, generating counterfactual explanations, and evaluating the resulting causal graph at every step. We highlight the potential of our framework via a physical robot manipulator on a pick-and-place task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
