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Causal belief is a cognitive practice that humans apply everyday to reasonabout cause and effect relations between factors, phenomena, or events. Likeoptical illusions, humans are prone to drawing causal relations between eventsthat are only coincidental (i.e., causal illusions). Researchers in domainssuch as cognitive psychology and healthcare often use logistically expensiveexperiments to understand causal beliefs and illusions. In this paper, wepropose Belief Miner, a crowdsourcing method for evaluating people's causalbeliefs and illusions. Our method uses the (dis)similarities between the causalrelations collected from the crowds and experts to surface the causal beliefsand illusions. Through an iterative design process, we developed a web-basedinterface for collecting causal relations from a target population. We thenconducted a crowdsourced experiment with 101 workers on Amazon Mechanical Turkand Prolific using this interface and analyzed the collected data with BeliefMiner. We discovered a variety of causal beliefs and potential illusions, andwe report the design implications for future research.

Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations