Quantitative methods in Human-Robot Interaction (HRI) research have primarily relied upon randomized, controlled experiments in laboratory settings. However, such experiments are not always feasible when external validity, ethical constraints, and ease of data collection are of concern. Furthermore, as consumer robots become increasingly available, increasing amounts of real-world data will be available to HRI researchers, which prompts the need for quantative approaches tailored to the analysis of observational data. In this article, we present an alternate approach towards quantitative research for HRI researchers using methods from causal inference that can enable researchers to identify causal relationships in observational settings where randomized, controlled experiments cannot be run. We highlight different scenarios that HRI research with consumer household robots may involve to contextualize how methods from causal inference can be applied to observational HRI research. We then provide a tutorial summarizing key concepts from causal inference using a graphical model perspective and link to code examples throughout the article, which are available at https://gitlab.com/causal/causal_hri. Our work paves the way for further discussion on new approaches towards observational HRI research while providing a starting point for HRI researchers to add causal inference techniques to their analytical toolbox.

翻译：在人机交互研究中，定量方法主要依赖于实验室环境下的随机对照实验。然而，当涉及外部有效性、伦理约束和数据采集便捷性时，此类实验并非总是可行。此外，随着消费级机器人的日益普及，人机交互研究者将获得越来越多的真实世界数据，这催生了针对观测数据分析的定量方法需求。本文提出了一种替代性的人机交互定量研究方法，借助因果推断方法帮助研究者在无法开展随机对照实验的观测环境中识别因果关系。我们通过消费级家庭机器人的人机交互研究案例，阐释因果推断方法在观测性人机交互研究中的具体应用场景。随后，我们从图模型视角出发，系统梳理因果推断的关键概念，并在全文配套提供代码示例（代码仓库：https://gitlab.com/causal/causal_hri）。本研究不仅为观测性人机交互研究的新方法探讨奠定基础，更为人机交互研究者将因果推断技术纳入分析工具箱提供了实践起点。