Causal belief is a cognitive practice that humans apply everyday to reason about cause and effect relations between factors, phenomena, or events. Like optical illusions, humans are prone to drawing causal relations between events that are only coincidental (i.e., causal illusions). Researchers in domains such as cognitive psychology and healthcare often use logistically expensive experiments to understand causal beliefs and illusions. In this paper, we propose Belief Miner, a crowdsourcing method for evaluating people's causal beliefs and illusions. Our method uses the (dis)similarities between the causal relations collected from the crowds and experts to surface the causal beliefs and illusions. Through an iterative design process, we developed a web-based interface for collecting causal relations from a target population. We then conducted a crowdsourced experiment with 101 workers on Amazon Mechanical Turk and Prolific using this interface and analyzed the collected data with Belief Miner. We discovered a variety of causal beliefs and potential illusions, and we report the design implications for future research.

翻译：因果信念是人类日常用于推理因素、现象或事件之间因果关系的认知实践。与视错觉类似，人类容易将仅具偶然性的事件关联为因果关系（即因果错觉）。认知心理学和医疗健康等领域的研究者通常采用成本高昂的实验来理解因果信念与错觉。本文提出信念挖掘者（Belief Miner）这一众包方法，用于评估人们的因果信念与错觉。该方法通过对比众包群体与专家所收集因果关系的（相异）相似性，来揭示因果信念与错觉。经迭代设计流程，我们开发了面向目标群体的因果关系采集网页界面，并在Amazon Mechanical Turk和Prolific平台上招募101名工作者开展众包实验，利用该界面收集数据并通过信念挖掘者进行分析。我们发现了多种因果信念及潜在错觉，并报告了未来研究的设计启示。