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  1. André Ariew (2003). Ernst Mayr's 'Ultimate/Proximate' Distinction Reconsidered and Reconstructed. 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. Jonathan Birch (2013). Hamilton's Rule and Its Discontents. British Journal for the Philosophy of Science:axt016.
    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. Thomas R. Grimes (1988). Statistical Explanation, Probability, and Counteracting Conditions. British Journal for the Philosophy of Science 39 (4):495-503.
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  4. Henrik Hållsten (1999). Deductive Chauvinism. 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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  5. Colin Howson (1988). 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. 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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  6. Colin Howson (1983). Statistical Explanation and Statistical Support. Erkenntnis 20 (1):61 - 78.
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  7. Gurol Irzik & Eric Meyer (1987). Causal Modeling: New Directions for Statistical Explanation. 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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  8. John L. King (1976). Statistical Relevance and Explanatory Classification. 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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  9. Hugh Lehman (1972). Statistical Explanation. 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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  10. Mohan Matthen (2010). What is Drift? A Response to Millstein, Skipper, and Dietrich. 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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  11. Mohan Matthen (2009). Drift and “Statistically Abstractive Explanation”. 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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  12. Edmund Nierlich (1988). Die Deduktiv-Nomologische Erklärung AlS Hauptmotiv Empirisch-Wissenschaftlicher Tätigkeit. 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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  13. Ilkka Niiniluoto (1981). Statistical Explanation Reconsidered. Synthese 48 (3):437 - 472.
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  14. Joseph C. Pitt (ed.) (1988). Theories of Explanation. 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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  15. Peter Railton (1978). A Deductive-Nomological Model of Probabilistic Explanation. 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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  16. David-Hillel Ruben (1989). The Ontology of Explanation. In Fred D'Agostino & I. C. Jarvie (eds.), Freedom and Rationality. Reidel. 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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  17. Wesley Salmon (1984). Scientific Explanation and the Causal Structure of the World. Princeton University Press.
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  18. Wesley C. Salmon (1985). Conflicting Conceptions of Scientific Explanation. Journal of Philosophy 82 (11):651-654.
  19. Wesley C. Salmon (1977). Hempel's Conception of Inductive Inference in Inductive-Statistical Explanation. 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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  20. Wesley C. Salmon (1971). Statistical Explanation & Statistical Relevance. [Pittsburgh]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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  21. Wesley C. Salmon (1965). The Status of Prior Probabilities in Statistical Explanation. 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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  22. Richard Schlegel (1970). Statistical Explanation in Physics: The Copenhagen Interpretation. 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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  23. M. Siebel (2011). Why Explanation and Thus Coherence Cannot Be Reduced to Probability. Analysis 71 (2):264-266.
  24. Lawrence Sklar (1973). Statistical Explanation and Ergodic Theory. 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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  25. Bradford Skow (2013). The Role of Chance in Explanation. 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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  26. Michael Strevens (2008). Depth: An Account of Scientific Explanation. 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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  27. Michael Strevens (2000). Do Large Probabilities Explain Better? 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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  28. Rasmus Grønfeldt Winther, Determinism and Total Explanation in the Biological and Behavioral Sciences. Encyclopedia of Life Sciences.
    Should we think of our universe as law-governed and “clockwork”-like or as disorderly and “soup”-like? Alternatively, should we consciously and intentionally synthesize these two extreme pictures? More concretely, how deterministic are the postulated causes and how rigid are the modeled properties of the best statistical methodologies used in the biological and behavioral sciences? The charge of this entry is to explore thinking about causation in the temporal evolution of biological and behavioral systems. Regression analysis and path analysis are simply explicated (...)
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  29. Katherine Yoshida, Mijke Rhemtulla & Athena Vouloumanos (2012). Exclusion Constraints Facilitate Statistical Word Learning. 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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