Answering Causal Queries about Singular Cases

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Causal reasoning is one of our most central cognitive competencies, which has attracted a substantial amount of research in the past decades. The main focus of this research has been on modelling judgments about general causal relations. For example, an indicator of a general causal relation between smoking and lung disease might be the finding that in a sample of people smokers tend to contract lung disease with a higher probability than non-smokers. However, there is a second class of judgments, judgments about singular causation, that have so far been largely neglected in the psychological literature. For example, when observing a specific smoker having contracted lung disease, the question can be raised whether this person's smoking is the cause of her lung disease, or whether the co-occurrence of the events is just a coincidence. Queries about singular causation are prevalent in everyday and professional contexts, such as the law or medicine. So far, singular causation has mainly been studied by philosophers who in most cases have restricted their focus on deterministic causal relations. By contrast, our goal is to study the more prevalent case of probabilistic causal relations, which raises the question how people distinguish singular causation from coincidence and from general causation. The project is subdivided into three parts: Project 1 focuses on elemental singular causal judgments with a single cause and a single effect. We will test a new Bayesian computational model, the structure induction model of singular causation (SISC), which we have recently developed to model responses to singular causation queries. The model assumes that people draw on their general causal knowledge when assessing singular causation queries. Moreover, it formalizes the assumption that people assess the likelihood that the cause has generated its effect given that both events have been observed to be present. Finally, the model assumes that people take into account uncertainty about the size of the causal model parameters and about the hypothesized causal structure. Project 1 tests whether people differentiate between general and singular causation judgments in a way predicted by SISC. Moreover, SISC will be tested against possible competitors. In Project 2 we study an extension of the basic model to account for mechanism knowledge, which has proven important in judgments about singular causation. Finally, Project 3 focuses on cases in which the reference class underlying the general causal relations can be flexibly chosen. The project addresses the question which reference class people choose when making judgments about general and singular causal relations. Additionally, we will investigate whether pragmatic factors influence how general versus singular causation queries are processed.