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  1. Ernst Mayr's 'Ultimate/Proximate' Distinction Reconsidered and Reconstructed.André Ariew - 2003 - Biology and Philosophy 18 (4):553-565.
    It's been 41 years since the publication of Ernst Mayr's Cause and Effect in Biology wherein Mayr most clearly develops his version of the influential distinction between ultimate and proximate causes in biology. In critically assessing Mayr's essay I uncover false statements and red-herrings about biological explanation. Nevertheless, I argue to uphold an analogue of the ultimate/proximate distinction as it refers to two different kinds of explanations, one dynamical the other statistical.
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  2. Hamilton's Rule and Its Discontents.Jonathan Birch - 2014 - British Journal for the Philosophy of Science 65 (2):381-411.
    In an incendiary 2010 Nature article, M. A. Nowak, C. E. Tarnita, and E. O. Wilson present a savage critique of the best-known and most widely used framework for the study of social evolution, W. D. Hamilton’s theory of kin selection. More than a hundred biologists have since rallied to the theory’s defence, but Nowak et al. maintain that their arguments ‘stand unrefuted’. Here I consider the most contentious claim Nowak et al. defend: that Hamilton’s rule, the core explanatory principle (...)
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  3. Theoretical Entities in Statistical Explanation.James G. Greeno - 1970 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1970:3 - 26.
  4. Statistical Explanation, Probability, and Counteracting Conditions.Thomas R. Grimes - 1988 - British Journal for the Philosophy of Science 39 (4):495-503.
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  5. Deductive Chauvinism.Henrik Hållsten - 1999 - Synthese 120 (1):49-59.
    Any theory of explanation must account for the explanatory successes of statistical scientific theories. This should not be done by endorsing determinism. These considerations have been taken as sufficient ground for rejecting the demand on explanations to be deductive. The arguments for doing so, in Coffa (1974) and Salmon (1977, 1984, 1988), are, however, not persuasive. Deductivism is a viable position. Considering that doubts can be raised against the explanatory validity of probabilistic causal relations and the intuitive plausibility of deductivism, (...)
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  6. On a Recent Argument for the Impossibility of a Statistical Explanation of Single Events, and a Defence of a Modified Form of Hempel's Theory of Statistical Explanation.Colin Howson - 1988 - Erkenntnis 29 (1):113 - 124.
    An argument has been recently proposed by Watkins, whose objective is to show the impossibility of a statistical explanation of single events. This present paper is an attempt to show that Watkins's argument is unsuccessful, and goes on to argue for an account of statistical explanation which has much in common with Hempel's classic treatment.
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  7. Statistical Explanation and Statistical Support.Colin Howson - 1983 - Erkenntnis 20 (1):61 - 78.
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  8. Causal Modeling: New Directions for Statistical Explanation.Gurol Irzik & Eric Meyer - 1987 - Philosophy of Science 54 (4):495-514.
    Causal modeling methods such as path analysis, used in the social and natural sciences, are also highly relevant to philosophical problems of probabilistic causation and statistical explanation. We show how these methods can be effectively used (1) to improve and extend Salmon's S-R basis for statistical explanation, and (2) to repair Cartwright's resolution of Simpson's paradox, clarifying the relationship between statistical and causal claims.
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  9. Statistical Relevance and Explanatory Classification.John L. King - 1976 - Philosophical Studies 30 (5):313 - 321.
    Numerous philosophers, among them Carl G. Hempel and Wesley C. Salmon, have attempted to explicate the notion of explanatory relevance in terms of the statistical relevance of various properties of an individual to the explanandum property itself (or what is here called narrow statistical relevance). This approach seems plausible if one assumes that to explain an occurrence is to show that it was to be expected or to exhibit its degree of expectability and the factors which influence its expectability. But (...)
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  10. Was Regression to the Mean Really the Solution to Darwin’s Problem with Heredity? [REVIEW]Adam Krashniak & Ehud Lamm - 2017 - Biology and Philosophy (5):1-10.
    Statistical reasoning is an integral part of modern scientific practice. In The Seven Pillars of Statistical Wisdom Stephen Stigler presents seven core ideas, or pillars, of statistical thinking and the historical developments of each of these pillars, many of which were concurrent with developments in biology. Here we focus on Stigler’s fifth pillar, regression, and his discussion of how regression to the mean came to be thought of as a solution to a challenge for the theory of natural selection. Stigler (...)
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  11. Statistical Explanation.Hugh Lehman - 1972 - Philosophy of Science 39 (4):500-506.
    Wesley Salmon has advanced a new model of explanations of particular facts which requires that the explanans contain laws. The laws used in explanations (according to this model) are of the form P(A· C1,B)=p1... P(A· Cn,B)=pn. A condition imposed by Salmon on these laws is that the reference classes, i.e. A· C1... A· Cn, be homogenous with reference to the property B. A reference class A is homogenous with reference to a property B if every property which determines a place (...)
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  12. What is Drift? A Response to Millstein, Skipper, and Dietrich.Mohan Matthen - 2010 - Philosophy and Theory in Biology 2 (20130604).
    The statistical interpretation of the Theory of Natural Selection claims that natural selection and drift are statistical features of mathematical aggregates of individual-level events. Natural selection and drift are not themselves causes. The statistical interpretation is motivated by a metaphysical conception of individual priority. Recently, Millstein, Skipper, and Dietrich (2009) have argued (a) that natural selection and drift are physical processes, and (b) that the statistical interpretation rests on a misconception of the role of mathematics in biology. Both theses are (...)
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  13. Drift and “Statistically Abstractive Explanation”.Mohan Matthen - 2009 - Philosophy of Science 76 (4):464-487.
    A hitherto neglected form of explanation is explored, especially its role in population genetics. “Statistically abstractive explanation” (SA explanation) mandates the suppression of factors probabilistically relevant to an explanandum when these factors are extraneous to the theoretical project being pursued. When these factors are suppressed, the explanandum is rendered uncertain. But this uncertainty traces to the theoretically constrained character of SA explanation, not to any real indeterminacy. Random genetic drift is an artifact of such uncertainty, and it is therefore wrong (...)
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  14. Philosophical Controversies in the Evaluation of Medical Treatments : With a Focus on the Evidential Roles of Randomization and Mechanisms in Evidence-Based Medicine.Alexander Mebius - 2015 - Dissertation, KTH Royal Institute of Technology
    This thesis examines philosophical controversies surrounding the evaluation of medical treatments, with a focus on the evidential roles of randomised trials and mechanisms in Evidence-Based Medicine. Current 'best practice' usually involves excluding non-randomised trial evidence from systematic reviews in cases where randomised trials are available for inclusion in the reviews. The first paper challenges this practice and evaluates whether adding of evidence from non-randomised trials might improve the quality and precision of some systematic reviews. The second paper compares the alleged (...)
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  15. Die Deduktiv-Nomologische Erklärung AlS Hauptmotiv Empirisch-Wissenschaftlicher Tätigkeit.Edmund Nierlich - 1988 - Erkenntnis 29 (1):1 - 33.
    In this paper an attempt is made at developing the notion of a real and complete empirical explanation as excluding all forms of potential or incomplete explanations. This explanation is, however, no longer conceived as the proper aim of empirical science, for it can certainly be gleaned from recent epistemological publications that no comprehensive notion of a real and complete scientific explanation is likely to be constructed from within empirical science. Contrary to common understanding the empirical explanation, deductive-nomological as well (...)
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  16. Statistical Explanation Reconsidered.Ilkka Niiniluoto - 1981 - Synthese 48 (3):437 - 472.
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  17. Some Problems of Causal Interpretation of Statistical Relationships.Stefan Nowak - 1960 - Philosophy of Science 27 (1):23-38.
    In following paper an attempt will be made to analyse the statistical relationships between variables as the functions of causal relations existing between them. Our basic assumption here is that statistical relationships between traits, events, or characteristics of objects, may be logically derived from the pattern of their mutual causal connections, if this pattern is described by appropriate concepts and with sufficient precision. The first part of the paper presents basic concepts, which according to author's view may serve for the (...)
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  18. Theories of Explanation.Joseph C. Pitt (ed.) - 1988 - Oxford University Press.
    Since the publication of Carl Hempel and Paul Oppenheim's ground-breaking work "Studies in the Logic of Explanation," the theory of explanation has remained a major topic in the philosophy of science. This valuable collection provides readers with the opportunity to study some of the classic essays on the theory of explanation along with the best examples of the most recent work being done on the topic. In addition to the original Hempel and Oppenheim paper, the volume includes Scriven's critical reaction (...)
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  19. A Deductive-Nomological Model of Probabilistic Explanation.Peter Railton - 1978 - Philosophy of Science 45 (2):206-226.
    It has been the dominant view that probabilistic explanations of particular facts must be inductive in character. I argue here that this view is mistaken, and that the aim of probabilistic explanation is not to demonstrate that the explanandum fact was nomically expectable, but to give an account of the chance mechanism(s) responsible for it. To this end, a deductive-nomological model of probabilistic explanation is developed and defended. Such a model has application only when the probabilities occurring in covering laws (...)
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  20. Explanation Beyond Causation? New Directions in the Philosophy of Scientific Explanation.Alexander Reutlinger - 2017 - Philosophy Compass 12 (2):e12395.
    In this paper, I aim to provide access to the current debate on non-causal explanations in philosophy of science. I will first present examples of non-causal explanations in the sciences. Then, I will outline three alternative approaches to non-causal explanations – that is, causal reductionism, pluralism, and monism – and, corresponding to these three approaches, different strategies for distinguishing between causal and non-causal explanation. Finally, I will raise questions for future research on non-causal explanations.
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  21. The Ontology of Explanation.David-Hillel Ruben - 1989 - In Fred D'Agostino & I. C. Jarvie (eds.), Freedom and Rationality. Reidel. pp. 67--85.
    In an explanation, what does the explaining and what gets explained? What are the relata of the explanation relation? Candidates include: people, events, facts, sentences, statements, and propositions.
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  22. Scientific Explanation and the Causal Structure of the World.Wesley Salmon - 1984 - Princeton University Press.
    The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...)
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  23. Conflicting Conceptions of Scientific Explanation.Wesley C. Salmon - 1985 - Journal of Philosophy 82 (11):651-654.
  24. Hempel's Conception of Inductive Inference in Inductive-Statistical Explanation.Wesley C. Salmon - 1977 - Philosophy of Science 44 (2):179-185.
    Carl G. Hempel has often stated that inductive-statistical explanations, as he conceives them, are inductive arguments. This discussion note raises the question of whether such arguments are to be understood as (1) arguments of the traditional sort, containing premises and conclusions, governed by some sort of inductive "acceptance rules," or (2) something more closely akin to Carnap's degree of confirmation statements which occur in an inductive logic which entirely eschews inductive "acceptance rules." Hempel's writings do not seem unequivocal on this (...)
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  25. Statistical Explanation & Statistical Relevance.Wesley C. Salmon - 1971 - University of Pittsburgh Press.
    Through his S–R model of statistical relevance, Wesley Salmon offers a solution to the scientific explanation of objectively improbable events.
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  26. The Status of Prior Probabilities in Statistical Explanation.Wesley C. Salmon - 1965 - Philosophy of Science 32 (2):137-146.
    A consideration of some basic problems that arise in the attempt to provide an adequate characterization of statistical explanation is taken to show that an understanding of the nature of scientific explanation requires us to deal with the philosophical problems connected with the nature of prior probabilities.
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  27. Statistical Explanation in Physics: The Copenhagen Interpretation.Richard Schlegel - 1970 - Synthese 21 (1):65 - 82.
    The statistical aspects of quantum explanation are intrinsic to quantum physics; individual quantum events are created in the interactions associated with observation and are not describable by predictive theory. The superposition principle shows the essential difference between quantum and non-quantum physics, and the principle is exemplified in the classic single-photon two-slit interference experiment. Recently Mandel and Pfleegor have done an experiment somewhat similar to the optical single-photon experiment but with two independently operated lasers; interference is obtained even with beam intensity (...)
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  28. Why Explanation and Thus Coherence Cannot Be Reduced to Probability.M. Siebel - 2011 - Analysis 71 (2):264-266.
    Some philosophers, most notably Hempel and Salmon, have tried to reduce explanation to probability by proposing analyses of explanation in probabilistic terms. Hempel claims, roughly, that a hypothesis H explains a datum D if and only if the conditional probability P is close to 1. It is well known that such an account fails in cases where H is irrelevant for D. Even though it is highly likely that Tom will not become pregnant, given that he regularly takes his wife’s (...)
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  29. Statistical Explanation and Ergodic Theory.Lawrence Sklar - 1973 - Philosophy of Science 40 (2):194-212.
    Some philosphers of science of an empiricist and pragmatist bent have proposed models of statistical explanation, but have then become sceptical of the adequacy of these models. It is argued that general considerations concerning the purpose of function of explanation in science which are usually appealed to by such philosophers show that their scepticism is not well taken; for such considerations provide much the same rationale for the search for statistical explanations, as these philosophers have characterized them, as they do (...)
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  30. The Role of Chance in Explanation.Bradford Skow - 2013 - Australasian Journal of Philosophy (1):1-21.
    ?Those ice cubes melted because by melting total entropy increased and entropy increase has a very high objective chance.? What role does the chance in this explanation play? I argue that it contributes to the explanation by entailing that the melting was almost necessary, and defend the claim that the fact that some event was almost necessary can, in the right circumstances, constitute a causal explanation of that event.
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  31. The Reference Class Problem in Evolutionary Biology: Distinguishing Selection From Drift.Michael Strevens - forthcoming - In Charles Pence & Grant Ramsey (eds.), Chance in Evolution.
    Evolutionary biology distinguishes differences in survival and reproduction rates due to selection from those due to drift. The distinction is usually thought to be founded in probabilistic facts: a difference in (say) two variants' average lifespans over some period of time that is due to selection is explained by differences in the probabilities relevant to survival; in the purest cases of drift, by contrast, the survival probabilities are equal and the difference in lifespans is a matter of chance. When there (...)
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  32. Depth: An Account of Scientific Explanation.Michael Strevens - 2008 - Harvard University Press.
    Approaches to explanation -- Causal and explanatory relevance -- The kairetic account of /D making -- The kairetic account of explanation -- Extending the kairetic account -- Event explanation and causal claims -- Regularity explanation -- Abstraction in regularity explanation -- Approaches to probabilistic explanation -- Kairetic explanation of frequencies -- Kairetic explanation of single outcomes -- Looking outward -- Looking inward.
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  33. Do Large Probabilities Explain Better?Michael Strevens - 2000 - Philosophy of Science 67 (3):366-390.
    It is widely held that the size of a probability makes no difference to the quality of a probabilistic explanation. I argue that explanatory practice in statistical physics belies this claim. The claim has gained currency only because of an impoverished conception of probabilistic processes and an unwarranted assumption that all probabilistic explanations have a single form.
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  34. Acceptable Risk.Cory Wimberly - 2015 - In The SAGE Encyclopedia of Economics and Society. SAGE.
    Perhaps the topic of acceptable risk never had a sexier and more succinct introduction than the one Edward Norton, playing an automobile company executive, gave it in Fight Club: “Take the number of vehicles in the field (A), multiply it by the probable rate of failure (B), and multiply the result by the average out of court settlement (C). A*B*C=X. If X is less than the cost of the recall, we don’t do one.” Of course, this dystopic scene also gets (...)
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  35. Exclusion Constraints Facilitate Statistical Word Learning.Katherine Yoshida, Mijke Rhemtulla & Athena Vouloumanos - 2012 - Cognitive Science 36 (5):933-947.
    The roles of linguistic, cognitive, and social-pragmatic processes in word learning are well established. If statistical mechanisms also contribute to word learning, they must interact with these processes; however, there exists little evidence for such mechanistic synergy. Adults use co-occurrence statistics to encode speech–object pairings with detailed sensitivity in stochastic learning environments (Vouloumanos, 2008). Here, we replicate this statistical work with nonspeech sounds and compare the results with the previous speech studies to examine whether exclusion constraints contribute equally to the (...)
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