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  1. Helen Beebee (2004). Chance-Changing Causal Processes. In Phil Dowe & Paul Noordhof (eds.), Cause and Chance: Causation in an Indeterministic World. Routledge.
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  2. Helen Beebee (1998). Do Causes Raise the Chances of Effects? Analysis 58 (3):182–190.
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  3. Helen Beebee (1997). Taking Hindrance Seriously. Philosophical Studies 88 (1):59-79.
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  4. Philip Bretzel (1977). Concerning a Probabilistic Theory of Causation Adequate for the Causal Theory of Time. Synthese 35 (2):173-190.
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  5. John Collins, Ned Hall & Laurie Paul (eds.) (2004). Causation and Counterfactuals. The Mit Press.
    Thirty years after Lewis's paper, this book brings together some of the most important recent work connecting—or, in some cases, disputing the connection ...
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  6. Phil Dowe (2004). Chance-Lowering Causes. In Phil Dowe & Paul Noordhof (eds.), Cause and Chance: Causation in an Indeterministic World. Routledge.
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  7. Phil Dowe (1999). The Conserved Quantity Theory of Causation and Chance Raising. Philosophy of Science 66 (3):501.
    In this paper I offer an 'integrating account' of singular causation, where the term 'integrating' refers to the following program for analysing causation. There are two intuitions about causation, both of which face serious counterexamples when used as the basis for an analysis of causation. The 'process' intuition, which says that causes and effects are linked by concrete processes, runs into trouble with cases of 'misconnections', where an event which serves to prevent another fails to do so on a particular (...)
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  8. Phil Dowe & Paul Noordhof (eds.) (2004). Cause and Chance: Causation in an Indeterministic World. Routledge.
    Cause and Chance is a collection of specially written papers by world-class metaphysicians. Its focus is the problems facing the "reductionist" approach to causation: the attempt to cover all types of causation, deterministic and indeterministic, with one basic theory.
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  9. Isabelle Drouet (2012). Causal Reasoning, Causal Probabilities, and Conceptions of Causation. Studies in History and Philosophy of Science Part C 43 (4):761-768.
  10. Dorothy Edgington (1997). Mellor on Chance and Causation. [REVIEW] British Journal for the Philosophy of Science 48 (3):411-433.
    Mellor's subject is singular causation between facts, expressed ‘E because C’. His central requirement for causation is that the chance that E if C be greater than the chance that E if C: chc(E)>chc(E). The book is as much about chance as it is about causation. I show that his way of distinguishing chc (E) from the traditional notion of conditional chance leaves than him with a problem about the existence of chQ(P) when Q is false (Section 3); and also (...)
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  11. Ellery Eells (1991). Probabilistic Causality. CUP.
    In this important first book in the series Cambridge Studies in Probability, Induction and Decision Theory, Ellery Eells explores and refines current ...
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  12. Frederick S. Elett & David P. Ericson (1986). An Analysis of Probabilistic Causation in Dichotomous Structures. Synthese 67 (2):175-193.
    During the past decades several philosophers of science and social scientists have been interested in the problems of causation. Recently attention has been given to probabilistic causation in dichotomous causal systems. The paper uses the basic features of probabilistic causation to argue that the causal modeling approaches developed by such researchers as Blalock (1964) and Duncan (1975) can provide, when an additional assumption is added, adequate qualitative measures of one variable causal influence upon another. Finally, some of the difficulties and (...)
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  13. J. Fetzer (ed.) (1988). Probability and Causality. D. Reidel.
  14. Stuart S. Glennan (1997). Probable Causes and the Distinction Between Subjective and Objective Chance. Noûs 31 (4):496-519.
    In this paper I present both a critical appraisal of Humphreys' probabilistic theory of causality and a sketch of an alternative view of the relationship between the notions of probability and of cause. Though I do not doubt that determinism is false, I claim that the examples used to motivate Humphreys' theory typically refer to subjective rather than objective chance. Additionally, I argue on a number of grounds that Humphreys' suggestion that linear regression models be used as a canonical form (...)
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  15. Bruce Glymour (2003). On the Metaphysics of Probabilistic Causation: Lessons From Social Epidemiology. Philosophy of Science 70 (5):1413-1423.
    I argue that the orthodox account of probabilistic causation, on which probabilistic causes determine the probability of their effects, is inconsistent with certain ontological assumptions implicit in scientific practice. In particular, scientists recognize the possibility that properties of populations can cause the behavior of members of the populations. Such emergent population level causation is metaphysically impossible on the orthodoxy.
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  16. Bruce Glymour (2003). On the Metaphysics of Probabilistic Causation: Lessons From Social Epidemiology. Philosophy of Science 70 (5):1413-1423.
    I argue that the orthodox account of probabilistic causation, on which probabilistic causes determine the probability of their effects, is inconsistent with certain ontological assumptions implicit in scientific practice. In particular, scientists recognize the possibility that properties of populations can cause the behavior of members of the populations. Such emergent population‐level causation is metaphysically impossible on the orthodoxy.
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  17. Luke Glynn (2011). A Probabilistic Analysis of Causation. British Journal for the Philosophy of Science 62 (2):343-392.
    The starting point in the development of probabilistic analyses of token causation has usually been the naïve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation by absence and omission. I show that (...)
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  18. C. Hitchcock (2004). Do All and Only Causes Raise the Probabilities of Effects? In J. Collins, E. J. Hall & L. A. Paul (eds.), Causation and Counterfactuals. MIT Press.
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  19. Christopher Hitchcock, Probabilistic Causation. Stanford Encyclopedia of Philosophy.
    “Probabilistic Causation” designates a group of theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes change the probabilities of their effects. This article traces developments in probabilistic causation, including recent developments in causal modeling. A variety of issues within, and objections to, probabilistic theories of causation will also be discussed.
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  20. Paul Humphreys (2004). Some Thoughts on Wesley Salmon's Contributions to the Philosophy of Probability. Philosophy of Science 71 (5):942-949.
    Wesley Salmon provided three classic criteria of adequacy for satisfactory interpretations of probability. A fourth criterion is suggested here. A distinction is drawn between frequency‐driven probability models and theory‐driven probability models and it is argued that single case accounts of chance are superior to frequency accounts at least for the latter. Finally it is suggested that theories of chance should be required only to be contingently true, a position which is a natural extension of Salmon's ontic account of probabilistic causality (...)
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  21. S. Kim (2001). Physical Process Theories and Token-Probabilistic Causation. Erkenntnis 54 (2):235-245.
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  22. Igal Kvart (2004). Probabilistic Cause, Edge Conditions, Late Preemption, and Discrete Cases. In Phil Dowe & Paul Noordhof (eds.), Cause and Chance: Causation in an Indeterministic World. Routledge. 163-188.
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  23. Igal Kvart (1986). A Theory of Counterfactuals. Hackett.
  24. Iain Martel, Probabilistic Empiricism: In Defence of a Reichenbachian Theory of Causation and the Direction of Time.
    A probabilistic theory of causation is a theory which holds that the central feature of causation is that causes (usually) raise the probability of their effects. In this dissertation, I defend Hans Reichenbach's original (1953) version of the probabilistic theory of causation, which analyses causal relations in terms of a three place statistical betweenness relation. Unlike most discussions of this theory, I hold that the statistical relation should be taken as a sufficient, but not as necessary, condition for causal betweenness. (...)
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  25. D. H. Mellor (1995). The Facts of Causation. Routledge.
    The Facts of Causation grapples with one of philosophy's most enduring issues. Causation is central to all of our lives. What we see and hear causes us to believe certain facts about the world. We need that information to know how to act and how to cause the effects we desire. D. H. Mellor, a leading scholar in the philosophy of science and metaphysics, offers a comprehensive theory of causation. Many questions (...)
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  26. D. H. Mellor (1988). On Raising the Chances of Effects. In J. Fetzer (ed.), Probability and Causality. D. Reidel.
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  27. Peter Menzies (1996). Probabilistic Causation and the Pre-Emption Problem. Mind 105 (417):85-117.
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  28. Peter Menzies (1989). Probabilistic Causation and Causal Processes: A Critique of Lewis. Philosophy of Science 56 (4):642-663.
    This paper examines a promising probabilistic theory of singular causation developed by David Lewis. I argue that Lewis' theory must be made more sophisticated to deal with certain counterexamples involving pre-emption. These counterexamples appear to show that in the usual case singular causation requires an unbroken causal process to link cause with effect. I propose a new probabilistic account of singular causation, within the framework developed by Lewis, which captures this intuition.
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  29. Thomas Müller (2005). Probability Theory and Causation: A Branching Space-Times Analysis. British Journal for the Philosophy of Science 56 (3):487 - 520.
    We provide a formally rigorous framework for integrating singular causation, as understood by Nuel Belnap's theory of causae causantes, and objective single case probabilities. The central notion is that of a causal probability space whose sample space consists of causal alternatives. Such a probability space is generally not isomorphic to a product space. We give a causally motivated statement of the Markov condition and an analysis of the concept of screening-off. Causal dependencies and probabilities 1.1 Background: causation in branching space-times (...)
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  30. Paul Noordhof (1999). Probabilistic Causation, Preemption and Counterfactuals. Mind 108 (429):95-125.
    Counter factual theories of Causation have had problems with cases of probabilistic causation and preemption. I put forward a counterfactual theory that seems to deal with these problematic cases and also has the virtue of providing an account of the alleged asymmetry between hasteners and delayers: the former usually being counted as causes, the latter not. I go on to consider a new type of problem case that has not received so much attention in the literature, those I dub catalysts (...)
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  31. Mika Oksanen (2002). Probabilistic Causation in Branching Time. Synthese 132 (1-2):89 - 117.
    A probabilistic and counterfactual theory of causality is developed within the framework of branching time. The theory combines ideas developed by James Fetzer, Donald Nute, Patrick Suppes, Ming Xu, John Pollock, David Lewis and Mellor among others.
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  32. Huw Price (1991). Agency and Probabilistic Causality. British Journal for the Philosophy of Science 42 (2):157-176.
    Probabilistic accounts of causality have long had trouble with ‘spurious’ evidential correlations. Such correlations are also central to the case for causal decision theory—the argument that evidential decision theory is inadequate to cope with certain sorts of decision problem. However, there are now several strong defences of the evidential theory. Here I present what I regard as the best defence, and apply it to the probabilistic approach to causality. I argue that provided a probabilistic theory appeals to the notions of (...)
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  33. Murali Ramachandran (2004). A Counterfactual Analysis of Indeterministic Causation. In J. Collins, E. J. Hall & L. A. Paul (eds.), Causation and Counterfactuals. MIT Press.
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  34. Murali Ramachandran (2004). Interdeterministic Causation and Varieties of Chance-Raising. In Phil Dowe & Paul Noordhof (eds.), Cause and Chance: Causation in an Indeterministic World. Routledge.
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  35. Murali Ramachandran (2000). Noordhof on Probabilistic Causation. Mind 109 (434):309-313.
    In a recent article, Paul Noordhof (1999) has put forward an intriguing account of causation intended to work under the assumption of indeterminism. I am going to present four problems for the account, three which challenge the necessity of the conditions he specifies, and one which challenges their joint-sufficiency.
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  36. Deborah A. Rosen (1978). In Defense of a Probabilistic Theory of Causality. Philosophy of Science 45 (4):604-613.
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  37. Jonathan Schaffer (2007). Review of Dowe and Noordhof: Cause and Chance: Causation in an Indeterministic World. [REVIEW] British Journal for the Philosophy of Science 58 (4):869-874.
    This is an excellent anthology. The contributors are first-rate, the contributions are state-of-the-art, and the content is highly unified. The introduction further connects the essays and succinctly articulates the main themes. What results will be of interest to anyone interested in the contemporary discussion of causation.
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  38. Jonathan Schaffer (2000). Overlappings: Probability-Raising Without Causation. Australasian Journal of Philosophy 78 (1):40 – 46.
    The leading regularity, counterfactual, and agential accounts of causation converge on the idea that causation is probability-raising. While the necessity of probability-raising for causation remains in dispute, the sufficiency of probability-raising for causation is generally assumed, at least in the direct (no intermediaries involved) and precisely described case. I offer a class of counterexamples: overlappings.
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  39. Michael Tooley (2004). Probability and Causation. In Phil Dowe & Paul Noordhof (eds.), Cause and Chance: Causation in an Indeterministic World. Routledge.
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  40. Charles R. Twardy & Kevin B. Korb (2004). A Criterion of Probabilistic Causation. Philosophy of Science 71 (3):241-262.
    The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebutted that idea. Another ill-conceived idea continues to haunt the debate, namely the idea that contextual unanimity can do the work of objective homogeneity. It cannot. We argue that only objective homogeneity in combination with a causal interpretation (...)
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  41. Erik Weber (2009). How Probabilistic Causation Can Account for the Use of Mechanistic Evidence. International Studies in the Philosophy of Science 23 (3):277-295.
    In a recent article in this journal, Federica Russo and Jon Williamson argue that an analysis of causality in terms of probabilistic relationships does not do justice to the use of mechanistic evidence to support causal claims. I will present Ronald Giere's theory of probabilistic causation, and show that it can account for the use of mechanistic evidence (both in the health sciences—on which Russo and Williamson focus—and elsewhere). I also review some other probabilistic theories of causation (of Suppes, Eells, (...)
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