Learning Causal Direction from Transitions with Continuous and Noisy Variables

Kevin SooUniversity of Pittsburgh, Pittsburgh, Pennsylvania, United States
Benjamin RottmanUniversity of Pittsburgh

Abstract

Previous work has found that one way people infer the direction of causal relationships involves identifying an asymmetry in how causes and effects change over time. In the current research we test the generalizability of this reasoning strategy in more complex environments involving ordinal and continuous variables and with noise. Participants were still able to use the strategy with ordinal and continuous variables. However, when noise made it difficult to identify the asymmetry participants were no longer able to infer the causal direction.

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Learning Causal Direction from Transitions with Continuous and Noisy Variables (340 KB)



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