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  1. Note on Simplicity and Statistical Explanations of Correlations.Chrysovalantis Stergiou - manuscript
    In this note, I discuss the simplicity of rival statistical explanations of a correlation, couched in terms of Reichenbachian Common Cause Systems. Simplicity is analyzed in two components, the so-called intrinsic and contextual simplicity. I show that if one disentangles simplicity from explanatory power then the size of the system provides an adequate for simplicity in both of its dimensions.
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  2. 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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  3. Explanation Through Representation, and its Limits.Bas Van Fraassen - forthcoming - Epistemologia.
  4. Some Reflections on the Statistical Postulate: Typicality, Probability and Explanation Between Deterministic and Indeterministic Theories.Valia Allori - 2020 - In Statistical Mechanics and Scientific Explanation: Determinism, Indeterminism and Laws of Nature, (2020). Singapore: World Scientific. pp. 65-111.
    A common way of characterizing Boltzmann’s explanation of thermodynamics in term of statistical mechanics is with reference to three ingredients: the dynamics, the past hypothesis, and the statistical postulate. In this paper I focus on the statistical postulate, and I have three aims. First, I wish to argue that regarding the statistical postulate as a probability postulate may be too strong: a postulate about typicality would be enough. Second, I wish to show that there is no need to postulate anything, (...)
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  5. Selection in a Complex World: Deriving Causality From Stable Equilibrium.Hugh Desmond - 2018 - Erkenntnis 83 (2):265-286.
    It is an ongoing controversy whether natural selection is a cause of population change, or a mere statistical description of how individual births and deaths accumulate. In this paper I restate the problem in terms of the reference class problem, and propose how the structure of stable equilibrium can provide a solution in continuity with biological practice. Insofar natural selection can be understood as a tendency towards equilibrium, key statisticalist criticisms are avoided. Further, in a modification of the Newtonian-force analogy, (...)
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  6. Was Regression to the Mean Really the Solution to Darwin’s Problem with Heredity?: Essay Review of Stigler, Stephen M. 2016. The Seven Pillars of Statistical Wisdom. Cambridge, Massachusetts: Harvard University Press. [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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  7. 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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  8. 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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  9. Causality and Unification: How Causality Unifies Statistical Regularities.Gerhard Schurz - 2015 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 30 (1):73-95.
    Two key ideas of scientific explanation−explanation as causal information and explanation as unification-have frequently been set into mutual opposition. This paper proposes a “dialectical solution” to this conflict, by arguing that causal explanations are preferable to non-causal ones, because they lead to a higherdegree of unification at the level of explaining statistical regularities. The core axioms of the theory of causal nets are justified because they offer the best if not the only unifying explanation of two statistical phenomena: screening off (...)
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  10. 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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  11. 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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  12. Explanation as Condition Satisfaction.Paul Humphreys - 2014 - Philosophy of Science 81 (5):1103-1116.
    It is shown that three common conditions for scientific explanations are violated by a widely used class of domain-independent explanations. These explanations can accommodate both complex and noncomplex systems and do not require the use of detailed models of system-specific processes for their effectiveness, although they are compatible with such model-based explanations. The approach also shows how a clean separation can be maintained between mathematical representations and empirical content.
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  13. 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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  14. Contrastive Statistical Explanation and Causal Heterogeneity.Jaakko Kuorikoski - 2012 - European Journal for Philosophy of Science 2 (3):435-452.
    Probabilistic phenomena are often perceived as being problematic targets for contrastive explanation. It is usually thought that the possibility of contrastive explanation hinges on whether or not the probabilistic behaviour is irreducibly indeterministic, and that the possible remaining contrastive explananda are token event probabilities or complete probability distributions over such token outcomes. This paper uses the invariance-under-interventions account of contrastive explanation to argue against both ideas. First, the problem of contrastive explanation also arises in cases in which the probabilistic behaviour (...)
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  15. 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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  16. 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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  17. Causal Modeling, Explanation and Severe Testing.Clark Glymour, Deborah G. Mayo & Aris Spanos - 2010 - In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. pp. 331-375.
  18. What is Drift? A Response to Millstein, Skipper, and Dietrich.Mohan Matthen - 2010 - Philosophy, Theory, and Practice 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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  19. 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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  20. 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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  21. 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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  22. Probabilistically, Explaining Things.Wesley C. Salmon - 2003 - In Kyburg Jr, E. Henry & Mariam Thalos (eds.), Probability is the Very Guide of Life: The Philosophical Uses of Chance. Open Court.
  23. Indeterministic Explanation: Visited, Revisited, and Again Revisited.William Ralph Seaman - 2002 - Dissertation, The University of Wisconsin - Madison
    It is widely accepted within philosophy of science that indeterministic explanation is possible. In this dissertation I attempt to show that the arguments supporting indeterministic explanation do not warrant the predominance of this view. The arguments play out across two areas of contention. The first concerns meta-theoretical principles that at one time commanded broad acceptance as conditions of adequacy for any proposed model of scientific explanation. These conditions include Principle P, which states that if A explains B, then A cannot (...)
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  24. Complexity, Self-Organization and Selection.Robert C. Richardson - 2001 - Biology and Philosophy 16 (5):653-682.
    Recent work on self organization promises an explanation of complex order which is independent of adaptation. Self-organizing systems are complex systems of simple units, projecting order as a consequence of localized and generally nonlinear interactions between these units. Stuart Kauffman offers one variation on the theme of self-organization, offering what he calls a ``statistical mechanics'' for complex systems. This paper explores the explanatory strategies deployed in this ``statistical mechanics,'' initially focusing on the autonomy of statistical explanation as it applies in (...)
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  25. 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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  26. 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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  27. Contrastive, Non-Probabilistic Statistical Explanations.Bruce Glymour - 1998 - Philosophy of Science 65 (3):448-471.
    Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of (...)
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  28. An Intervening Cause Counterexample to Railton's DNP Model of Explanation.Stuart Gluck & Steven Gimbel - 1997 - Philosophy of Science 64 (4):692-697.
    Peter Railton (1978) has introduced the influential deductive-nomological-probabilistic (DNP) model of explanation which is the culmination of a tradition of formal, non-pragmatic accounts of scientific explanation. The other models in this tradition have been shown to be susceptible to a class of counterexamples involving intervening causes which speak against their sufficiency. This treatment has never been extended to the DNP model; we contend that the usual form of these counterexamples is ineffective in this case. However, we develop below a new (...)
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  29. Rethinking Objective Homogeneity: Statistical Versus Ontic Approaches.Richard N. Burnor - 1993 - Philosophical Studies 71 (3):307 - 325.
  30. What’s Wrong with Salmon’s History: The Third Decade.James H. Fetzer - 1992 - Philosophy of Science 59 (2):246-262.
    My purpose here is to elaborate the reasons I maintain that Salmon has not been completely successful in reporting the history of work on explanation. The most important limitation of his account is that it does not emphasize the critical necessity to embrace a suitable conception of probability in the development of the theory of probabilistic explanation.
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  31. Explanation in Physics: Explanation in Physical Theory: Peter Clark.Peter Clark - 1990 - Royal Institute of Philosophy Supplement 27:155-175.
    The corpus of physical theory is a paradigm of knowledge. The evolution of modern physical theory constitutes the clearest exemplar of the growth of knowledge. If the development of physical theory does not constitute an example of progress and growth in what we know about the Universe nothing does. So anyone interested in the theory of knowledge must be interested consequently in the evolution and content of physical theory. Crucial to the conception of physics as a paradigm of knowledge is (...)
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  32. Boston Studies in the Philosophy of Science Vol. 122: Statistics in Science.Roger Cooke & Domenico Costantini (eds.) - 1990 - Springer Verlag.
  33. Explanation in Physics: Explanation*: Michael Redhead.Michael Redhead - 1990 - Royal Institute of Philosophy Supplement 27:135-154.
    In what sense do the sciences explain? Or do they merely describe what is going on without answering why-questions at all. But cannot description at an appropriate ‘level’ provide all that we can reasonably ask of an explanation? Well, what do we mean by explanation anyway? What, if anything, gets left out when we provide a so-called scientific explanation? Are there limits of explanation in general, and scientific explanation, in particular? What are the criteria for a good explanation? Is it (...)
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  34. Average Explanations.Alan Nelson - 1989 - Erkenntnis 30 (1-2):23 - 42.
    Good scientific explanations sometimes appear to make use of averages. Using concrete examples from current economic theory, I argue that some confusions about how averages might work in explanations lead to both philosophical and economic problems about the interpretation of the theory. I formulate general conditions on potentially proper uses of averages to refine a notion of average explanation. I then try to show how this notion provides a means for resolving longstanding philosophical problems in economics and other quantitative social (...)
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  35. 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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  36. 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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  37. 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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  38. 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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  39. Theories of Explanation.Joseph 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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  40. Explanation, Subjunctives and Statistical Theories.Del Ratzsch - 1988 - International Studies in the Philosophy of Science 3 (1):80 – 96.
  41. Explanation, Subjunctives and Statistical Theories.Del Ratzsch - 1988 - International Studies in the Philosophy of Science 3 (1):80-96.
    (1988). Explanation, subjunctives and statistical theories. International Studies in the Philosophy of Science: Vol. 3, No. 1, pp. 80-96. doi: 10.1080/02698598808573326.
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  42. 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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  43. Explanation and Causation. [REVIEW]Elliott Sober - 1987 - British Journal for the Philosophy of Science 38 (2):243-257.
  44. Review of E Xplanation and Causation. [REVIEW]Elliott Sober - 1987 - British Journal for the Philosophy of Science 38 (2):243 - 257.
  45. On an Information-Theoretic Model of Explanation.James Woodward - 1987 - Philosophy of Science 54 (1):21-44.
    This paper is an assessment of an attempt, by James Greeno, to measure the explanatory power of statistical theories by means of the notion of transmitted information (It). It is argued that It has certain features that are inappropriate in a measure of explanatory power. In particular, given a statistical theory T with explanans variables St and explanandum variables Mj, it is argued that no plausible measure of explanatory power should depend on the probability P(Si) of occurrence of initial conditions (...)
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  46. Conflicting Conceptions of Scientific Explanation.Wesley C. Salmon - 1985 - Journal of Philosophy 82 (11):651-654.
  47. Scientific Explanation and the Causal Structure of the World.Wesley C. 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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  48. Common Cause Explanation.Elliott Sober - 1984 - Philosophy of Science 51 (2):212-241.
    Russell (1948), Reichenbach (1956), and Salmon (1975, 1979) have argued that a fundamental principle of science and common sense is that "matching" events should not be chalked up to coincidence, but should be explained by postulating a common cause. Reichenbach and Salmon provided this intuitive idea with a probabilistic formulation, which Salmon used to argue for a version of scientific realism. Van Fraassen (1980, 1982) showed that the principle, so construed, runs afoul of certain results in quantum mechanics. In this (...)
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  49. Statistical Explanation and Statistical Support.Colin Howson - 1983 - Erkenntnis 20 (1):61 - 78.
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  50. Probabilistic Explanation: Introduction.Wesley C. Salmon - 1982 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:179 - 180.
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