Cognitive Science 39 (1):65-95 (2015)

Abstract
Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented a causal Bayes net model with separate error sources for causes and effects. In several experiments, we tested this new model using the size of Markov violations as the empirical indicator of differential assumptions about the sources of error. As predicted by the model, the size of Markov violations was influenced by the location of the agents and was moderated by the causal structure and the type of causal variables
Keywords Markov condition  Causal Bayes nets  Agency  Causal dispositions  Causal reasoning
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DOI 10.1111/cogs.12132
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References found in this work BETA

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
Causation, Prediction, and Search.Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.

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