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Abstract
Safety purports to explain why cases of accidentally true belief are not knowledge, addressing Gettier cases and cases of belief based on statistical evidence. However, numerous examples suggest that safety fails as a condition on knowledge: a belief can be safe even when one's evidence is clearly insufficient for knowledge and knowledge is compatible with the nearby possibility of error, a situation ruled out by the safety condition. In this paper, I argue for a new modal condition designed to capture the non-accidental relationship between facts and evidence required for knowledge: causal safety. I argue that possible errors in belief can be captured by accounting for deviations in causal relationships and that there is a natural way to characterize which causal errors are relevant in an epistemic situation. Using this, I develop a causal analogue to safety, where one's belief in p is causally safe iff it is true in all causally relevant worlds where one believes p. Causal safety, I argue, can explain the cases safety is meant to address while avoiding the issues raised for safety.
Keywords Causal Models  Knowledge  Relevant Alternatives  Contextualism
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References found in this work BETA

Knowledge and Lotteries.John Hawthorne - 2003 - Oxford, England: Oxford University Press.
Elusive Knowledge.David K. Lewis - 1996 - Australasian Journal of Philosophy 74 (4):549 – 567.
Knowledge and Its Limits.Timothy Williamson - 2000 - Philosophy 76 (297):460-464.
Causality.Judea Pearl - 2000 - Cambridge University Press.
Knowledge and Its Limits.Timothy Williamson - 2005 - Philosophy and Phenomenological Research 70 (2):452-458.

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