Cognitive Science 34 (1):148-160 (2010)

Authors
Tamar Kushnir
Cornell University
Alison Gopnik
University of California, Berkeley
Abstract
We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. We replicated these findings with less‐informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge.
Keywords Causal Bayes nets  Psychology  Causal inference  Causal reasoning  Human experimentation  Graphical representation
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DOI 10.1111/j.1551-6709.2009.01072.x
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References found in this work BETA

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
Causality.Judea Pearl - 2000 - Cambridge University Press.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.

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Citations of this work BETA

Grounding: It’s (Probably) All in the Head.Kristie Miller & James Norton - 2017 - Philosophical Studies 174 (12):3059-3081.

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