Causal reasoning without mechanism

What is it about?

Unobservable mechanisms tie causes to their effects generate observable events. Being able to understand generating mechanisms is an essential survival skill. But how can one make inferences about hidden causal structures? In this paper, we introduced the "domain matching heuristic" to explain how humans perform causal reasoning when lacking scientific knowledge about a phenomenon. People tend to (1) reduce the otherwise vast space of possible causal relations by focusing only on the likeliest ones, and (2) think about possible effects that participate in the same domain.

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Why is it important?

Given that in science and everyday life, we often rely on causal reasoning skills to understand about the world surrounding us, such inferential skill should be investigated in the light of more than one theory. I think this paper is a reminder that there is no one-size-fits-all approach to studying causality. It explores the logical relationships between causal factors, which is a good example of non-probabilistic approaches focusing on the kinds of logical arguments and helping to expand our understanding about people’s reasoning styles.

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