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Evaluating Causal Hypotheses: The Curious Case of Correlated Cues
Abstract
Although the causal graphical model framework has achieved considerable success accounting for causal learning data, application of that formalism to multi-cause situations assumes that people are insensitive to the statistical properties of the causes themselves. The present experiment tests this assumption by first instructing subjects on a causal model consisting of two independent and generative causes and then requesting them to make data likelihood judgments, that is, to estimate the probability of some data given the model. The correlation between the causes in the data was either positive, zero, or negative. The data was judged as most likely in the positive condition and least likely in the negative condition, a finding that obtained even though all other statistical properties of the data (e.g., causal strengths, outcome density) were controlled. These results pose a problem for current models of causal learning.