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  1. Commentary on Mathias Frisch's Causal Reasoning in Physics.Ney Alyssa - manuscript
    This is a commentary on Mathias Frisch's book Causal Reasoning in Physics (Cambridge 2014). This commentary was presented at the 2016 Pacific Division Meeting of the American Philosophical Association in a session sponsored by the Society for the Metaphysics of Science.
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  2. The Dualist’s Dilemma: The High Cost of Reconciling Neuroscience with a Soul.Keith Augustine & Yonatan I. Fishman - 2015 - In Keith Augustine & Michael Martin (eds.), The Myth of an Afterlife: The Case against Life After Death. Rowman & Littlefield. pp. 203-292.
    Tight correlations between mental states and brain states have been observed time and again within the ethology of biologically ingrained animal behaviors, the comparative psychology of animal minds, the evolutionary psychology of mental adaptations, the behavioral genetics of inherited mental traits, the developmental psychology of the maturing mind, the psychopharmacology of mind-altering substances, and cognitive neuroscience more generally. They imply that our mental lives are only made possible because of brain activity—that having a functioning brain is a necessary condition for (...)
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  3. Category Structure Affects the Developmental Trajectory of Children's Inductive Inferences for Both Natural Kinds and Artefacts.Julia R. Badger & Laura R. Shapiro - 2015 - Thinking and Reasoning 21 (2):206-229.
    Inductive reasoning is fundamental to human cognition, yet it remains unclear how we develop this ability and what might influence our inductive choices. We created novel categories in which crucial factors such as domain and category structure were manipulated orthogonally. We trained 403 4–9-year-old children to categorise well-matched natural kind and artefact stimuli with either featural or relational category structure, followed by induction tasks. This wide age range allowed for the first full exploration of the developmental trajectory of inductive reasoning (...)
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  4. Representing Personal Determinants in Causal Structures.Albert Bandura - 1984 - Psychological Review 91 (4):508-511.
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  5. Learning Causal Structure From Reasoning.Aron Barbey & Phillip Wolff - unknown
    According to the transitive dynamics model, people can construct causal structures by linking together configurations of force. The predictions of the model were tested in two experiments in which participants generated new causal relationships by chaining together two (Experiment 1) or three (Experiment 2) causal relations. The predictions of the transitive dynamics model were compared against those of Goldvarg and Johnson-Laird’s model theory (Goldvarg & Johnson- Laird, 2001). The transitive dynamics model consistently predicted the overall causal relationship drawn by participants (...)
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  6. Hume's Defence of Causal Inference. [REVIEW]François Beets - 2000 - Dialogue 39 (2):404-406.
  7. A Logic for the Discovery of Deterministic Causal Regularities.Mathieu Beirlaen, Bert Leuridan & Frederik Van De Putte - forthcoming - Synthese:1-33.
    We present a logic, \, for the discovery of deterministic causal regularities starting from empirical data. Our approach is inspired by Mackie’s theory of causes as INUS-conditions, and implements a more recent adjustment to Mackie’s theory according to which the left-hand side of causal regularities is required to be a minimal disjunction of minimal conjunctions. To derive such regularities from a given set of data, we make use of the adaptive logics framework. Our knowledge of deterministic causal regularities is, as (...)
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  8. What Causal Conditional Reasoning Tells Us About People's Understanding of Causality.Sieghard Beller & Gregory Kuhnm - 2007 - Thinking and Reasoning 13 (4):426 – 460.
    Causal conditional reasoning means reasoning from a conditional statement that refers to causal content. We argue that data from causal conditional reasoning tasks tell us something not only about how people interpret conditionals, but also about how they interpret causal relations. In particular, three basic principles of people's causal understanding emerge from previous studies: the modal principle, the exhaustive principle, and the equivalence principle. Restricted to the four classic conditional inferences—Modus Ponens, Modus Tollens, Denial of the Antecedent, and Affirmation of (...)
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  9. Exposing the Causal Structure of Processes by Learning CP-Logic Programs.Hendrik Blockeel - 2008 - In Tu-Bao Ho & Zhi-Hua Zhou (eds.), Pricai 2008: Trends in Artificial Intelligence. Springer. pp. 2--2.
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  10. The Relationship Between Moral Judgment, Causal Reasoning and General Reasoning.Ira Brooks‐Walsh & Edmund V. Sullivan - 1973 - Journal of Moral Education 2 (2):131-136.
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  11. A Search for Causal Mechanisms Guides Learning in Novel Domains.Al Brown - 1986 - Bulletin of the Psychonomic Society 24 (5):334-334.
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  12. Edge Replacement and Nonindependence in Causation.D. Buchanan, J. Tenenbaum & D. Sobel - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  13. The Induction of Hidden Causes: Causal Mediation and Violations of Independent Causal Influence.Christopher D. Carroll & Patricia W. Cheng - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 913--918.
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  14. What Are Randomised Controlled Trials Good For?Nancy Cartwright - 2010 - Philosophical Studies 147 (1):59 - 70.
    Randomized controlled trials (RCTs) are widely taken as the gold standard for establishing causal conclusions. Ideally conducted they ensure that the treatment ‘causes’ the outcome—in the experiment. But where else? This is the venerable question of external validity. I point out that the question comes in two importantly different forms: Is the specific causal conclusion warranted by the experiment true in a target situation? What will be the result of implementing the treatment there? This paper explains how the probabilistic theory (...)
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  15. Childrens Inferences About Ambiguous Events-the Effects of Foregrounding and Multiple Clues.Ma Casteel - 1992 - Bulletin of the Psychonomic Society 30 (6):477-477.
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  16. Causal Attribution and Crossing Over Between Probabilities in Clinical Diagnosis.Daniele Chiffi & Pierdaniele Giaretta - 2013 - In Christer Svennerlind, Jan Almäng & Rögnvaldur Ingthorsson (eds.), Johanssonian Investigations. Ontos Verlag. pp. 5--191.
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  17. Evidential Variety as a Source of Credibility for Causal Inference: Beyond Sharp Designs and Structural Models.François Claveau - 2011 - Journal of Economic Methodology 18 (3):233-253.
    There is an ongoing debate in economics between the design-based approach and the structural approach. The main locus of contention regards how best to pursue the quest for credible causal inference. Each approach emphasizes one element ? sharp study designs versus structural models ? but these elements have well-known limitations. This paper investigates where a researcher might look for credibility when, for the causal question under study, these limitations are binding. It argues that seeking variety of evidence ? understood specifically (...)
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  18. The Supposed Competition Between Theories of Human Causal Inference.David Danks - 2005 - Philosophical Psychology 18 (2):259 – 272.
    Newsome ((2003). The debate between current versions of covariation and mechanism approaches to causal inference. Philosophical Psychology, 16, 87-107.) recently published a critical review of psychological theories of human causal inference. In that review, he characterized covariation and mechanism theories, the two dominant theory types, as competing, and offered possible ways to integrate them. I argue that Newsome has misunderstood the theoretical landscape, and that covariation and mechanism theories do not directly conflict. Rather, they rely on distinct sets of reliable (...)
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  19. The Epistemology of Causal Judgment.David Joseph Danks - 2001 - Dissertation, University of California, San Diego
    We make constant use of causal beliefs in our everyday lives without giving much thought to the source of those beliefs, even for situations about which we have no specific prior causal knowledge. We can ask two distinct types of questions about these causal judgments: descriptive questions and normative questions . The primary goal of this dissertation is to apply normative research on causal judgment to our descriptive theories. ;I begin this dissertation by describing the primary results of research on (...)
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  20. Applications of the Adaptive Logic for Causal Discovery.Leen De Vreese & Erik Weber - 2004 - Logique Et Analyse 185 (188):33-51.
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  21. On the Theoretical Limits to Reliable Causal Inference.Benoit Desjardins - 1999 - Dissertation, University of Pittsburgh
    One of the most central problems in scientific research is the search for explanations of some aspect of nature for which empirical data is available. One seeks to identify the causal processes explaining the data, in the form of a model of the aspect of nature under study. Although traditional statistical approaches are excellent for finding statistical dependencies in a body of empirical data, they prove inadequate at finding the causal structure in the data. New graphical algorithmic approaches have been (...)
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  22. On 'Stabilising' Medical Mechanisms, Truth-Makers and Epistemic Causality: A Critique to Williamson and Russo's Approach.Stefan Dragulinescu - 2012 - Synthese 187 (2):785-800.
    In this paper I offer an anti-Humean critique to Williamson and Russo’s approach to medical mechanisms. I focus on one of the specific claims made by Williamson and Russo, namely the claim that micro-structural ‘mechanisms’ provide evidence for the stability across populations of causal relationships ascertained at the (macro-) level of (test) populations. This claim is grounded in the epistemic account of causality developed by Williamson, an account which—while not relying exclusively on mechanistic evidence for justifying causal judgements—appeals nevertheless to (...)
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  23. Causal Reasoning, Causal Probabilities, and Conceptions of Causation.Isabelle Drouet - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (4):761-768.
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  24. A Cognitive Neuroscience Framework for Understanding Causal Reasoning and the Law.Jonathan Fugelsang & Dunbar & Kevin - 2006 - In Semir Zeki & Oliver Goodenough (eds.), Law and the Brain. Oxford University Press.
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  25. Direct Causes and the Trouble with Soft Interventions.Frederick Eberhardt - 2014 - Erkenntnis 79 (4):1-23.
    An interventionist account of causation characterizes causal relations in terms of changes resulting from particular interventions. I provide a new example of a causal relation for which there does not exist an intervention satisfying the common interventionist standard. I consider adaptations that would save this standard and describe their implications for an interventionist account of causation. No adaptation preserves all the aspects that make the interventionist account appealing. Part of the fallout is a clearer account of the difficulties in characterizing (...)
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  26. Introduction to the Epistemology of Causation.Frederick Eberhardt - 2009 - Philosophy Compass 4 (6):913-925.
    This survey presents some of the main principles involved in discovering causal relations. They belong to a large array of possible assumptions and conditions about causal relations, whose various combinations limit the possibilities of acquiring causal knowledge in different ways. How much and in what detail the causal structure can be discovered from what kinds of data depends on the particular set of assumptions one is able to make. The assumptions considered here provide a starting point to explore further the (...)
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  27. The Logic of Causal Methods in Social Science.Frederick S. Ellett & David P. Ericson - 1983 - Synthese 57 (1):67-82.
    Two kinds of causal inference rules which are widely used by social scientists are investigated. Two conceptions of causation also widely used are explicated — the INUS and probabilistic conceptions of causation. It is shown that the causal inference rules which link correlation, a kind of partial correlation, and a conception of causation areinvalid. It is concluded anew methodology is required for causal inference.
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  28. Causal Conditional Reasoning and Conditional Likelihood.Philip M. Fernbach & Adam Darlow - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1088--1093.
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  29. Causal Reasoning in Physics.Mathias Frisch - 2014 - Cambridge University Press.
    Much has been written on the role of causal notions and causal reasoning in the so-called 'special sciences' and in common sense. But does causal reasoning also play a role in physics? Mathias Frisch argues that, contrary to what influential philosophical arguments purport to show, the answer is yes. Time-asymmetric causal structures are as integral a part of the representational toolkit of physics as a theory's dynamical equations. Frisch develops his argument partly through a critique of anti-causal arguments and partly (...)
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  30. 'The Most Sacred Tenet'? Causal Reasoning in Physics.Mathias Frisch - 2009 - British Journal for the Philosophy of Science 60 (3):459-474.
    According to a view widely held among philosophers of science, the notion of cause has no legitimate role to play in mature theories of physics. In this paper I investigate the role of what physicists themselves identify as causal principles in the derivation of dispersion relations. I argue that this case study constitutes a counterexample to the popular view and that causal principles can function as genuine factual constraints. IntroductionCausality and Dispersion RelationsNorton's SkepticismConclusion.
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  31. A Cognitive Neuroscience Framework for Understanding Causal Reasoning and the Law.Jonathan A. Fugelsang & Kevin N. Dunbar - 2006 - In Semir Zeki & Oliver Goodenough (eds.), Law and the Brain. Oxford University Press. pp. 157--166.
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  32. Causal Inference.C. Glymour, P. Spirtes & R. Scheines - 1991 - Erkenntnis 35 (1-3):151 - 189.
    We have examined only a few of the basic questions about causal inference that result from Reichenbach's two principles. We have not considered what happens when the probability distribution is a mixture of distributions from different causal structures, or how unmeasured common causes can be detected, or what inferences can reliably be drawn about causal relations among unmeasured variables, or the exact advantages that experimental control offers. A good deal is known about these questions, and there is a good deal (...)
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  33. Learning Causes: Psychological Explanations of Causal Explanation. [REVIEW]Clark Glymour - 1998 - Minds and Machines 8 (1):39-60.
    I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, (...)
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  34. Causal Learning Mechanisms in Very Young Children: Two-, Three-, and Four-Year-Olds Infer Causal Relations From Patterns of Variation and Covariation.Clark Glymour, Alison Gopnik, David M. Sobel & Laura E. Schulz - unknown
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  35. Exploring Causal Structure with the TETRAD Program.Clark Glymour, Richard Scheines & Peter Spirtes - unknown
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  36. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.Clark Glymour, Richard Scheines, Peter Spirtes & Kevin T. Kelly - unknown
    Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
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  37. Young Children Infer Causal Strength From Probabilities and Interventions.Alison Gopnik - unknown
    Word count (excluding abstract and references): 2,498 words. Address for correspondence: T. Kushnir, Psychology Department, University of California, 3210 Tolman Hall #1650, Berkeley, CA 94720-1650. Phone: 510-205-9847. Fax: 510-642- 5293. E-mail: tkushnir@berkeley.edu.
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  38. Answering Questions About Events in Causal Mechanisms.Ac Graesser & Dd Hemphill - 1989 - Bulletin of the Psychonomic Society 27 (6):508-508.
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  39. Learning Causality in a Complex World: Understandings of Consequence.Tina Grotzer - 2012 - Rowman & Littlefield Education.
    Introduction -- Simple linear causality : one thing makes another happen -- The cognitive science of simple causality : why do we get stuck? -- Domino causality : effects that become causes -- Cyclic causality : loops and feedback -- Spiraling causality : escalation and de-escalation -- Mutual causality : symbiosis and bi-directionality -- Relational causality : balances and differentials -- Across time and distance : detecting delayed and distant effects -- "What happened?" vs. "what's going on?" : thinking about (...)
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  40. Causality, Uncertainty and Ignorance.Rolf Haenni & Stephan Hartmann - 2006 - Minds and Machines 16 (3).
    Special issue. With contributions by Malcolm Forster, Rocio Garcia-Rotamero and Ulrich Hoffrage, Christian Jakob, Kevin Korb and Erik Nyberg, Michael Smithson, Daniel Steel, Brad Weslake, and Jon Williamson.
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  41. Causal Bayes Nets as Psychological Theories of Causal Reasoning: Evidence From Psychological Research.York Hagmayer - 2016 - Synthese 193 (4):1107-1126.
    Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal structures, to induce causal structure from data and to derive predictions. Causal Bayes nets have been used as psychological theories in at least two ways. They were used as rational, computational models of causal reasoning and they were used as formal models of mental causal models. A crucial assumption made by them is the Markov condition, which informally states that variables are (...)
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  42. Causal Reasoning Through Intervention.York Hagmayer, Steven A. Sloman, David A. Lagnado & Michael R. Waldmann - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
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  43. OAE: The Ontology of Adverse Events.Yongqun He, Sirarat Sarntivijai, Yu Lin, Zuoshuang Xiang, Abra Guo, Shelley Zhang, Desikan Jagannathan, Luca Toldo, Cui Tao & Barry Smith - 2014 - Journal of Biomedical Semantics 5 (29):29.
    A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description: The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data (...)
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  44. Causal and Symbolic Understanding in Historical Epistemology.Michael Heidelberger - 2011 - Erkenntnis 75 (3):467-482.
    The term “historical epistemology” can be read in two different ways: (1) as referring to a program of ‘historicizing’ epistemology, in the sense of a critique of traditional epistemology’s tendency to gloss over historical context, or (2) as a manifesto of ‘epistemologizing’ history, i.e. as a critique of radical historicist and relativist approaches. In this paper I will defend a position in this second sense. I show that one can account for the historical development and diversity of science without disavowing (...)
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  45. When Functions and Causes Compete.Daniel Heussen - 2010 - Thinking and Reasoning 16 (3):233-250.
    The discounting principle states that 'the role of a given cause in producing a given effect is discounted if other plausible causes are present' (Kelley, 1972, p. 8). The principle has only been tested with cases where the two explanations are of the same kind (i.e., causal explanations). However, explanations of properties of objects, people, or events often involve functions. Zebras have stripes in order to be better camouflaged. Humans have eyebrows to keep sweat from running into their eyes. Adrenaline (...)
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  46. Contemporary Science and Natural Explanation: Commonsense Conceptions of Causality.Denis J. Hilton (ed.) - 1988 - New York University Press.
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  47. Knowledge-Based Causal Attribution: The Abnormal Conditions Focus Model.Denis J. Hilton & Ben R. Slugoski - 1986 - Psychological Review 93 (1):75-88.
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  48. Causal Reasoning.Christoph Hoerl - 2011 - Philosophical Studies 152 (2):167-179.
    The main focus of this paper is the question as to what it is for an individual to think of her environment in terms of a concept of causation, or causal concepts, in contrast to some more primitive ways in which an individual might pick out or register what are in fact causal phenomena. I show how versions of this question arise in the context of two strands of work on causation, represented by Elizabeth Anscombe and Christopher Hitchcock, respectively. I (...)
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  49. Perception, Causal Understanding, and Locality.Christoph Hoerl - 2011 - In Johannes Roessler, Hemdat Lerman & Naomi Eilan (eds.), Perception, Causation, and Objectivity. Oxford University Press. pp. 207.
    Contemporary philosophical debates about causation are dominated by two approaches, which are often referred to as difference-making and causal process approaches to causation, respectively. I provide a characterization of the dialectic between these two approaches, on which that dialectic turns crucially on the question as to whether our common sense concept of causation involves a commitment to locality – i.e., to the claim that causal relations are always subject to spatial constraints. I then argue that we can extract from existing (...)
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  50. Understanding Counterfactuals, Understanding Causation: Issues in Philosophy and Psychology.Christoph Hoerl, Teresa McCormack & Sarah R. Beck (eds.) - 2011 - Oxford:: Oxford University Press.
    How are causal judgements such as 'The ice on the road caused the traffic accident' connected with counterfactual judgements such as 'If there had not been any ice on the road, the traffic accident would not have happened'? This volume throws new light on this question by uniting, for the first time, psychological and philosophical approaches to causation and counterfactuals. Traditionally, philosophers have primarily been interested in connections between causal and counterfactual claims on the level of meaning or truth-conditions. More (...)
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