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  1. Ingo Brigandt (forthcoming). Evolutionary Developmental Biology and the Limits of Philosophical Accounts of Mechanistic Explanation. In P.-A. Braillard and C. Malaterre (ed.), Explanation in Biology: An Enquiry into the Diversity of Explanatory Patterns in the Life Sciences. Springer.
    Evolutionary developmental biology (evo-devo) is considered a ‘mechanistic science,’ in that it causally explains morphological evolution in terms of changes in developmental mechanisms. Evo-devo is also an interdisciplinary and integrative approach, as its explanations use contributions from many fields and pertain to different levels of organismal organization. Philosophical accounts of mechanistic explanation are currently highly prominent, and have been particularly able to capture the integrative nature of multifield and multilevel explanations. However, I argue that evo-devo demonstrates the need for a (...)
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  2. David Michael Kaplan & Carl F. Craver (2011). The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective. Philosophy of Science 78 (4):601-627.
    We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are (...)
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  3. Nicholas Rescher (1970). Scientific Explanation. New York,Free Press.
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  4. David-Hillel Ruben (ed.) (1993). Explanation. Oxford University Press.
    The aim of this series is to bring together important recent writings in major areas of philosophical inquiry, selected from a variety of sources, mostly periodicals, which may not be conveniently available to the university student or the general reader. The editor of each volume contributes an introductory essay on the items chosen and on the questions with which they deal. A selective bibliography is appended as a guide to further reading. This volume presents a selection of the most important (...)
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  5. David-Hillel Ruben (1990). Explaining Explanation. Routledge.
    Getting our Bearings The series in which this book is appearing is called 'The Problems of Philosophy: Their Past and Present'; this volume, ...
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  6. Kari L. Theurer (2014). Seventeenth-Century Mechanism: An Alternative Framework for Reductionism. Philosophy of Science 80 (5):907-918.
    The current antireductionist consensus rests in part on the indefensibility of the deductive-nomological model of explanation, on which classical reductionism depends. I argue that the DN model is inessential to the reductionist program and that mechanism provides a better framework for thinking about reductionism. This runs counter to the contemporary mechanists’ claim that mechanism is an alternative to reductionism. I demonstrate that mechanists are committed to reductionism, as evidenced by the historical roots of the contemporary mechanist program. This view shares (...)
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  7. Jeroen van Bouwel, Erik Weber & Leen de Vreese (2011). Indispensability Arguments in Favour of Reductive Explanations. Journal for General Philosophy of Science 42 (1):33-46.
    Instances of explanatory reduction are often advocated on metaphysical grounds; given that the only real things in the world are subatomic particles and their interaction, we have to try to explain everything in terms of the laws of physics. In this paper, we show that explanatory reduction cannot be defended on metaphysical grounds. Nevertheless, indispensability arguments for reductive explanations can be developed, taking into account actual scientific practice and the role of epistemic interests. Reductive explanations might be indispensable to address (...)
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  8. Erik Weber, Jeroen Van Bouwel & Leen De Vreese (2013). Scientific Explanation. Springer.
    When scientist investigate why things happen, they aim at giving an explanation. But what does a scientific explanation look like? In the first chapter (Theories of Scientific Explanation) of this book, the milestones in the debate on how to characterize scientific explanations are exposed. The second chapter (How to Study Scientific Explanation?) scrutinizes the working-method of three important philosophers of explanation, Carl Hempel, Philip Kitcher and Wesley Salmon and shows what went wrong. Next, it is the responsibility of current philosophers (...)
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Causal Accounts of Explanation
  1. Daniela Bailer-Jones, Monika Dullstein & Sabina Pauen (eds.) (2007). Kausales Denken: Philosophische und Psychologische Perspektiven. Mentis.
    Kausales Denken spielt sowohl im Alltag wie auch im wissenschaftlichen Forschungsprozess eine zentrale Rolle. Es erlaubt uns, Phänomene vorherzusagen, zu kontrollieren und zu verstehen. Kausales Denken geht über die Angabe der Ursachen eines Phänomens hinaus: Wollen wir verstehen, warum ein Fahrrad fährt, so versuchen wir, Schritt für Schritt nachzuvollziehen, wie die einzelnen Bestandteile des Fahrrads zusammenwirken, um miteinander die Bewegung zu produzieren. Wir sind an dem Mechanismus interessiert, durch den das Phänomen zustande kommt. Dieses Vorgehen wird in der Wissenschaftsphilosophie wie (...)
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  2. Ingo Brigandt (2013). Explanation in Biology: Reduction, Pluralism, and Explanatory Aims. Science and Education 22 (1):69-91.
    This essay analyzes and develops recent views about explanation in biology. Philosophers of biology have parted with the received deductive-nomological model of scientific explanation primarily by attempting to capture actual biological theorizing and practice. This includes an endorsement of different kinds of explanation (e.g., mathematical and causal-mechanistic), a joint study of discovery and explanation, and an abandonment of models of theory reduction in favor of accounts of explanatory reduction. Of particular current interest are philosophical accounts of complex explanations that appeal (...)
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  3. Alex Broadbent (2011). Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press. 45--69.
    This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie their (...)
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  4. L. R. Franklin-Hall, The Emperor's New Mechanisms.
    This paper argues that the increasingly dominant new mechanistic approach to scientific explanation, as developed to date, does not shed new light on explanatory practice. First, I systematize the explanatory account, one according to which explanations are mechanistic models that satisfy three desiderata: 1) they must represent causal relations, 2) describe the proper parts, and 3) depict the system at the right ‘level.’ Then I argue that even the most promising attempts to flesh out these constraints have fallen far short. (...)
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  5. L. R. Franklin-Hall (forthcoming). Explaining Causal Selection with Explanatory Causal Economy: Biology and Beyond. In P.-A. Braillard & C. Malaterre (eds.), Explanation in Biology: An Enquiry into the Diversity of Explanatory Patterns in the Life Sciences. Springer.
    Among the factors necessary for the occurrence of some event, which of these are selectively highlighted in its explanation and labeled as causes — and which are explanatorily omitted, or relegated to the status of background conditions? Following J. S. Mill, most have thought that only a pragmatic answer to this question was possible. In this paper I suggest we understand this ‘causal selection problem’ in causal-explanatory terms, and propose that explanatory trade-offs between abstraction and stability can provide a principled (...)
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  6. L. R. Franklin-Hall (forthcoming). High-Level Explanation and the Interventionist's 'Variables Problem'. British Journal for the Philosophy of Science.
    The interventionist account of causal explanation, in the version presented by Jim Woodward (2003), has been recently claimed capable of buttressing the widely felt—though poorly understood—hunch that high-level, relatively abstract explanations, of the sort provided by sciences like biology, psychology and economics, are in some cases explanatorily optimal. It is the aim of this paper to show that this is mistaken. Due to a lack of effective constraints on the causal variables at the heart of the interventionist causal-explanatory scheme, as (...)
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  7. Toby Handfield, Charles R. Twardy, Kevin B. Korb & Graham Oppy (2008). The Metaphysics of Causal Models: Where's the Biff? Erkenntnis 68 (2):149-68.
    This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases of late preëmption and other cases (...)
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  8. Richard Healey (2013). How Quantum Theory Helps Us Explain. British Journal for the Philosophy of Science:axt031.
    I offer an account of how the quantum theory we have helps us explain so much. The account depends on a pragmatist interpretation of the theory: this takes a quantum state to serve as a source of sound advice to physically situated agents on the content and appropriate degree of belief about matters concerning which they are currently inevitably ignorant. The general account of how to use quantum states and probabilities to explain otherwise puzzling regularities is then illustrated by showing (...)
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  9. Peter Hedström & Petri Ylikoski (2010). Causal Mechanisms in the Social Sciences. Annual Review of Sociology 36:49–67.
    During the past decade, social mechanisms and mechanism-based ex- planations have received considerable attention in the social sciences as well as in the philosophy of science. This article critically reviews the most important philosophical and social science contributions to the mechanism approach. The first part discusses the idea of mechanism- based explanation from the point of view of philosophy of science and relates it to causation and to the covering-law account of explanation. The second part focuses on how the idea (...)
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  10. M. C. W. Janssen & Y. -H. Tan (1991). Why Friedman's Non-Monotonic Reasoning Defies Hempel's Covering Law Model. Synthese 86 (2):255 - 284.
    In this paper we will show that Hempel's covering law model can't deal very well with explanations that are based on incomplete knowledge. In particular the symmetry thesis, which is an important aspect of the covering law model, turns out to be problematic for these explanations. We will discuss an example of an electric circuit, which clearly indicates that the symmetry of explanation and prediction does not always hold. It will be argued that an alternative logic for causal explanation is (...)
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  11. Monte Ransome Johnson (2009). The Aristotelian Explanation of the Halo. Apeiron 42 (4):325-357.
    For an Aristotelian observer, the halo is a puzzling phenomenon since it is apparently sublunary, and yet perfectly circular. This paper studies Aristotle's explanation of the halo in Meteorology III 2-3 as an optical illusion, as opposed to a substantial thing (like a cloud), as was thought by his predecessors and even many successors. Aristotle's explanation follows the method of explanation of the Posterior Analytics for "subordinate" or "mixed" mathematical-physical sciences. The accompanying diagram described by Aristotle is one of the (...)
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  12. Kareem Khalifa (2011). Contrastive Explanations as Social Accounts. Social Epistemology 24 (4):263-284.
    Explanatory contrastivists hold that we often explain phenomena of the form p rather than q. In this paper, I present a new, social‐epistemological model of contrastive explanation—accountabilism. Specifically, my view is inspired by social‐scientific research that treats explanations fundamentally as accounts; that is, communicative actions that restore one's social status when charged with questionable behaviour. After developing this model, I show how accountabilism provides a more comprehensive model of contrastive explanation than the causal models of contrastive explanation that are currently (...)
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  13. Harold Kincaid (2004). Contextualism, Explanation and the Social Sciences. Philosophical Explorations 7 (3):201 – 218.
    Debates about explanation in the social sciences often proceed without any clear idea what an 'account' of explanation should do. In this paper I take a stance - what I will call contextualism - that denies there are purely formal and conceptual constraints on explanation and takes standards of explanation to be substantive empirical claims, paradigmatically claims about causation. I then use this standpoint to argue for position on issues in the philosophy of social science concerning reduction, idealized models, social (...)
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  14. Jeff Kochan (2010). Contrastive Explanation and the 'Strong Programme' in the Sociology of Scientific Knowledge. Social Studies of Science 40 (1):127-44.
    In this essay, I address a novel criticism recently levelled at the Strong Programme by Nick Tosh and Tim Lewens. Tosh and Lewens paint Strong Programme theorists as trading on a contrastive form of explanation. With this, they throw valuable new light on the explanatory methods employed by the Strong Programme. However, as I shall argue, Tosh and Lewens run into trouble when they accuse Strong Programme theorists of unduly restricting the contrast space in which legitimate historical and sociological explanations (...)
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  15. Jaakko Kuorikoski & Petri Ylikoski (2013). How Organization Explains. In. In Vassilios Karakostas & Dennis Dieks (eds.), Epsa11 Perspectives and Foundational Problems in Philosophy of Science. Springer. 69--80.
  16. James Ladyman (2003). Stathis Psillos,Causation and Explanation. Chesham: Acumen, 2002. [REVIEW] Metascience 12 (3):431-434.
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  17. Marc Lange (2012). Abstraction and Depth in Scientific Explanation. [REVIEW] Philosophy and Phenomenological Research 84 (2):483-491.
  18. Arnon Levy (2014). Machine-Likeness and Explanation by Decomposition. Philosophers' Imprint 14 (6).
    Analogies to machines are commonplace in the life sciences, especially in cellular and molecular biology — they shape conceptions of phenomena and expectations about how they are to be explained. This paper offers a framework for thinking about such analogies. The guiding idea is that machine-like systems are especially amenable to decompositional explanation, i.e., to analyses that tease apart underlying components and attend to their structural features and interrelations. I argue that for decomposition to succeed a system must exhibit causal (...)
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  19. Arnon Levy & William Bechtel (2013). Abstraction and the Organization of Mechanisms. Philosophy of Science 80 (2):241-261.
  20. David Lewis (1986). Causal Explanation. In , Philosophical Papers Vol. Ii. Oxford University Press. 214-240.
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  21. David Lewis (1986). Philosophical Papers Vol. Ii. Oxford University Press.
  22. Lennart Nordenfelt & B. Ingemar B. Lindahl (eds.) (1984). Health, Disease, and Causal Explanations in Medicine. Reidel.
  23. Robert Northcott (2013). Degree of Explanation. Synthese 190 (15):3087-3105.
    Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures (...)
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  24. Garrett Pendergraft (2011). In Defense of a Causal Requirement on Explanation. In Phyllis McKay Illari Federica Russo (ed.), Causality in the Sciences. Oxford University Press. 470.
    Causalists about explanation claim that to explain an event is to provide information about the causal history of that event. Some causalists also endorse a proportionality claim, namely that one explanation is better than another insofar as it provides a greater amount of causal information. In this chapter I consider various challenges to these causalist claims. There is a common and influential formulation of the causalist requirement – the ‘Causal Process Requirement’ – that does appear vulnerable to these anti-causalist challenges, (...)
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  25. 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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  26. Angela Potochnik (forthcoming). Causal Patterns and Adequate Explanations. Philosophical Studies:1-20.
    Causal accounts of scientific explanation are currently broadly accepted (though not universally so). My first task in this paper is to show that, even for a causal approach to explanation, significant features of explanatory practice are not determined by settling how causal facts bear on the phenomenon to be explained. I then develop a broadly causal approach to explanation that accounts for the additional features that I argue an explanation should have. This approach to explanation makes sense of several aspects (...)
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  27. Angela Potochnik (2011). Explanation and Understanding. European Journal for Philosophy of Science 1 (1):29-38.
    Michael Strevens offers an account of causal explanation according to which explanatory practice is shaped by counterbalanced commitments to representing causal influence and abstracting away from overly specific details. In this paper, I challenge a key feature of that account. I argue that what Strevens calls explanatory frameworks figure prominently in explanatory practice because they actually improve explanations. This suggestion is simple but has far-reaching implications. It affects the status of explanations that cite multiply realizable properties; changes the explanatory role (...)
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  28. Angela Potochnik (2010). Explanatory Independence and Epistemic Interdependence: A Case Study of the Optimality Approach. British Journal for the Philosophy of Science 61 (1):213-233.
    The value of optimality modeling has long been a source of contention amongst population biologists. Here I present a view of the optimality approach as at once playing a crucial explanatory role and yet also depending on external sources of confirmation. Optimality models are not alone in facing this tension between their explanatory value and their dependence on other approaches; I suspect that the scenario is quite common in science. This investigation of the optimality approach thus serves as a case (...)
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  29. Stathis Psillos (2007). Causal Explanation and Manipulation. In. In Johannes Persson & Petri Ylikoski (eds.), Rethinking Explanation. Springer. 93--107.
  30. Jani Raerinne (2013). Explanatory, Predictive, and Heuristic Roles of Allometries and Scaling Relationships. BioScience 63 (3):191-198.
    Allometries and scaling relationships have become popular among biologists. One reason for this popularity is the generality of these relationships, which has provided authors with hope that allometries and scaling relationships represent biological laws and/or explanatory generalizations. I will discuss three roles of allometries and scaling relationships: the explanatory, the predictive, and the heuristic roles. I will argue that allometries and scaling relationships often function successfully heuristically, that is, by discovering or elucidating phenomena-to-be-explained or “patterns” from data rather than making (...)
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  31. Henk W. De Regt & Dennis Dieks (2005). A Contextual Approach to Scientific Understanding. Synthese 144 (1):137 - 170.
    Achieving understanding of nature is one of the aims of science. In this paper we offer an analysis of the nature of scientific understanding that accords with actual scientific practice and accommodates the historical diversity of conceptions of understanding. Its core idea is a general criterion for the intelligibility of scientific theories that is essentially contextual: which theories conform to this criterion depends on contextual factors, and can change in the course of time. Our analysis provides a general account of (...)
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  32. Alexander Reutlinger (forthcoming). Why Is There Universal Macro-Behavior? Renormalization Group Explanation As Non-Causal Explanation. Philosophy of Science.
    Renormalization group (RG) methods are an established strategy to explain how it is possible that microscopically different systems exhibit virtually the same macro behavior when undergoing phase-transitions. I argue – in agreement with Robert Batterman – that RG explanations are non-causal explanations. However, Batterman misidentifies the reason why RG explanations are non-causal: it is not the case that an explanation is non- causal if it ignores causal details. I propose an alternative argument, according to which RG explanations are non-causal explanations (...)
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  33. David-Hillel Ruben (1994). A Counterfactual Theory of Causal Explanation. Noûs 28 (4):465-481.
  34. 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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  35. Wesley C. Salmon (1998). Causality and Explanation. Oxford University Press.
    Wesley Salmon is renowned for his seminal contributions to the philosophy of science. He has powerfully and permanently shaped discussion of such issues as lawlike and probabilistic explanation and the interrelation of explanatory notions to causal notions. This unique volume brings together twenty-six of his essays on subjects related to causality and explanation, written over the period 1971-1995. Six of the essays have never been published before and many others have only appeared in obscure venues. The volume includes a section (...)
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  36. Wesley C. Salmon (1997). Causality and Explanation: A Reply to Two Critiques. Philosophy of Science 64 (3):461-477.
    This paper discusses several distinct process theories of causality offered in recent years by Phil Dowe and me. It addresses problems concerning the explication of causal process, causal interaction, and causal transmission, whether given in terms of transmission of marks, transmission of invariant or conserved quantities, or mere possession of conserved quantities. Renouncing the mark-transmission and invariant quantity criteria, I accept a conserved quantity theory similar to Dowe's--differing basically with respect to causal transmission. This paper also responds to several fundamental (...)
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  37. Wesley C. Salmon (1985). Conflicting Conceptions of Scientific Explanation. Journal of Philosophy 82 (11):651-654.
  38. David H. Sanford (2009). Causation. In Jaegwon Kim, Ernest Sosa & Gary S. Rosenkrantz (eds.), A Companion to Metaphysics. Wiley-Blackwell.
    Twenty-one paragraphs in this entry begin with a statement of a view about causation. To help organize the entry, the next sentence then classifies the view as 'prevailing, majority, controversial', or 'minority'. The following brief discussions attempt to be clear and fair. Respect for fairness, however, does not prevent the author from referring to his own views. For example, the author classifies "There is no element of genuine a priori reasoning in causal inference" as a majority view. After expounded the (...)
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  39. 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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  40. 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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  41. Michael Strevens (2004). The Causal and Unification Approaches to Explanation Unified—Causally. Noûs 38 (1):154–176.
    The two major modern accounts of explanation are the causal and unification accounts. My aim in this paper is to provide a kind of unification of the causal and the unification accounts, by using the central technical apparatus of the unification account to solve a central problem faced by the causal account, namely, the problem of determining which parts of a causal network are explanatorily relevant to the occurrence of an explanandum. The end product of my investigation is a causal (...)
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  42. Jonathan Waskan (2011). Mechanistic Explanation at the Limit. Synthese 183 (3):389-408.
    Resurgent interest in both mechanistic and counterfactual theories of explanation has led to a fair amount of discussion regarding the relative merits of these two approaches. James Woodward is currently the pre-eminent counterfactual theorist, and he criticizes the mechanists on the following grounds: Unless mechanists about explanation invoke counterfactuals, they cannot make sense of claims about causal interactions between mechanism parts or of causal explanations put forward absent knowledge of productive mechanisms. He claims that these shortfalls can be offset if (...)
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