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  1. Antireductionist Interventionism.Reuben Stern & Benjamin Eva - forthcoming - British Journal for the Philosophy of Science.
    Kim’s causal exclusion argument purports to demonstrate that the non-reductive physicalist must treat mental properties (and macro-level properties in general) as causally inert. A number of authors have attempted to resist Kim’s conclusion by utilizing the conceptual resources of Woodward’s (2005) interventionist conception of causation. The viability of these responses has been challenged by Gebharter (2017a), who argues that the causal exclusion argument is vindicated by the theory of causal Bayesian networks (CBNs). Since the interventionist conception of causation relies crucially (...)
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  2. Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS.Stefano Canali - 2016 - Big Data and Society 3 (2).
    Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal knowledge is necessary for (...)
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  3. Causation.Douglas Kutach - 2014 - Malden, MA: Polity.
    In most academic and non-academic circles throughout history, the world and its operation have been viewed in terms of cause and effect. The principles of causation have been applied, fruitfully, across the sciences, law, medicine, and in everyday life, despite the lack of any agreed-upon framework for understanding what causation ultimately amounts to. In this engaging and accessible introduction to the topic, Douglas Kutach explains and analyses the most prominent theories and examples in the philosophy of causation. The book is (...)
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  4. A Probabilistic Analysis of Causation.Luke Glynn - 2011 - British Journal for the Philosophy of Science 62 (2):343-392.
    The starting point in the development of probabilistic analyses of token causation has usually been the naïve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation by absence and omission. I show that (...)
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  5. Natural-born determinists: a new defense of causation as probability-raising.Robert Northcott - 2010 - Philosophical Studies 150 (1):1-20.
    A definition of causation as probability-raising is threatened by two kinds of counterexample: first, when a cause lowers the probability of its effect; and second, when the probability of an effect is raised by a non-cause. In this paper, I present an account that deals successfully with problem cases of both these kinds. In doing so, I also explore some novel implications of incorporating into the metaphysical investigation considerations of causal psychology.
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  6. How effects depend on their causes, why causal transitivity fails, and why we care about causation.Gunnar Björnsson - 2007 - Philosophical Studies 133 (3):349-390.
    Despite recent efforts to improve on counterfactual theories of causation, failures to explain how effects depend on their causes are still manifest in a variety of cases. In particular, theories that do a decent job explaining cases of causal preemption have problems accounting for cases of causal intransitivity. Moreover, the increasing complexity of the counterfactual accounts makes it difficult to see why the concept of causation would be such a central part of our cognition. In this paper, I propose an (...)
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  7. Pearson’s Wrong Turning: Against Statistical Measures of Causal Efficacy.Robert Northcott - 2005 - Philosophy of Science 72 (5):900-912.
    Standard statistical measures of strength of association, although pioneered by Pearson deliberately to be acausal, nowadays are routinely used to measure causal efficacy. But their acausal origins have left them ill suited to this latter purpose. I distinguish between two different conceptions of causal efficacy, and argue that: 1) Both conceptions can be useful 2) The statistical measures only attempt to capture the first of them 3) They are not fully successful even at this 4) An alternative definition more squarely (...)
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  8. Causation, Prediction, and Search.Peter Spirtes, Clark Glymour, Scheines N. & Richard - 2000 - Mit Press: Cambridge.
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  9. Storrs McCall's Tree of The Universe and Counterfactuals شجرة الكون وقضايا مناقضة الواقع عند ستورس مكال.Salah Osman - 1999 - Journal of the Faculty of Arts, Menoufia University 2956-2090 39:83 – 128.
    لم يكن العلم يومًا غريبًا عن الفلسفة، بل لقد أدت العلاقة الجدلية المستمرة بينهما إلى مزيد من المصداقية والقبول لبعض النماذج التفسيرية، سواء منها ما كان في الأصل نموذجًا فلسفيًا ثم وجد دعمًا علميًا – كنموذج الذرات والفـراغ عند «ديمرقــريطس» وتبنى النظرية الذرية الحديثة لإطاره العام – أو ما كان منها نموذجًا علميًا تم تداوله فلسفيًا – كنموذج النقاط المادية المتجاذبة عند »نيوتن« وتبنى معظم فلاسفة الحقبة الحديثة له. وفي هذا المقال يهتم الدكتور صلاح عثمان بمناقشة نموذج شجرة الكون لفيلسوف (...)
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  10. IX*—Would Cause.Adam Morton - 1981 - Proceedings of the Aristotelian Society 81 (1):139-152.
    I describe ways in which it is easier to analyse causation in the consequent of a conditional: what an event would cause if it occurred. I consider some possiblereasons forthis.
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