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Abstract
One approach to knowledge, termed the relevant alternatives theory, stipulates that a belief amounts to knowledge if one can eliminate all relevant alternatives to the belief in the epistemic situation. This paper uses causal graphical models to formalize the relevant alternatives approach to knowledge. On this theory, an epistemic situation is encoded through the causal relationships between propositions, which determine which alternatives are relevant and irrelevant. This formalization entails that statistical evidence is not sufficient for knowledge, provides a simple way to incorporate epistemic contextualism, and can rule out many Gettier cases from knowledge. The interpretation in terms of causal models offers more precise predictions for the relevant alternatives theory, strengthening the case for it as a theory of knowledge.
Keywords Causal Models  Knowledge  Relevant Alternatives  Contextualism
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References found in this work BETA

Knowledge and Lotteries.John Hawthorne - 2003 - Oxford University Press.
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Epistemic Operators.Fred I. Dretske - 1970 - Journal of Philosophy 67 (24):1007-1023.

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