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  1. Causation, Norm Violation, and Culpable Control.Alicke Mark, Rose David & Bloom Dori - 2011 - Journal of Philosophy 108 (12):670-696.
    Causation is one of philosophy's most venerable and thoroughly-analyzed concepts. However, the study of how ordinary people make causal judgments is a much more recent addition to the philosophical arsenal. One of the most prominent views of causal explanation, especially in the realm of harmful or potentially harmful behavior, is that unusual or counternormative events are accorded privileged status in ordinary causal explanations. This is a fundamental assumption in psychological theories of counterfactual reasoning, and has been transported to philosophy by (...)
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  2. 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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  3. 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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  4. 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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  5. Representing Personal Determinants in Causal Structures.Albert Bandura - 1984 - Psychological Review 91 (4):508-511.
    Addresses the substantive issues raised by J. E. Staddon's (see record 1985-04009-001) alternative models of causality, in response to Staddon's displeasure with what he claimed to be the present author's (see record 1983-22326-001) formalization of causal structures. The major question at issue is not the formalizability of causal processes but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures. Evidence indicates that the residuum of past stimuli cannot serve as an adequate proxy for cognitive processes, (...)
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  6. 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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  7. Detecting Causal Chains in Small-N Data.Michael Baumgartner - 2013 - Field Methods 25:3-24.
    The first part of this paper shows that Qualitative Comparative Analysis (QCA)--also in its most recent forms as presented in Ragin (2000, 2008)--, does not correctly analyze data generated by causal chains, which, after all, are very common among causal processes in the social sciences. The incorrect modeling of data originating from chains essentially stems from QCA’s reliance on Quine-McCluskey optimization to eliminate redundancies from sufficient and necessary conditions. Baumgartner (2009a,b) has introduced a Boolean methodology, termed Coincidence Analysis (CNA), that (...)
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  8. The Causal Chain Problem.Michael Baumgartner - 2008 - Erkenntnis 69 (2):201-226.
    This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertinent empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains are one of the most ubiquitous (macroscopic) causal structures is underdetermined by empirical data. It will be argued that even though the chain and its associated common cause model are empirically equivalent there exists an important asymmetry between (...)
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  9. Identifying Intervention Variables.Michael Baumgartner & Isabelle Drouet - 2013 - European Journal for Philosophy of Science 3 (2):183-205.
    The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as so-called intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal discovery, the first part of this paper shows that the interventionist nature of variables cannot, in principle, be established based only on an interventionist notion of causation. The second part then demonstrates that standard observational methods that draw on Bayesian networks identify intervention (...)
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  10. Constitutive Relevance, Mutual Manipulability, and Fat-Handedness.Michael Baumgartner & Alexander Gebharter - 2016 - British Journal for the Philosophy of Science 67 (3):731-756.
    The first part of this paper argues that if Craver’s ([2007a], [2007b]) popular mutual manipulability account (MM) of mechanistic constitution is embedded within Woodward’s ([2003]) interventionist theory of causation--for which it is explicitly designed--it either undermines the mechanistic research paradigm by entailing that there do not exist relationships of constitutive relevance or it gives rise to the unwanted consequence that constitution is a form of causation. The second part shows how Woodward’s theory can be adapted in such a way that (...)
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  11. Hume's Defence of Causal Inference. [REVIEW]François Beets - 2000 - Dialogue 39 (2):404-406.
  12. A Logic for the Discovery of Deterministic Causal Regularities.Mathieu Beirlaen, Bert Leuridan & Frederik Van De Putte - 2018 - Synthese 195 (1):367-399.
    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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  13. 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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  14. Newton's Regulae Philosophandi.Zvi Biener - 2018 - In Chris Smeenk & Eric Schliesser (eds.), Oxford Handbook of Isaac Newton. Oxford University Press.
    Newton’s Regulae philosophandi—the rules for reasoning in natural philosophy—are maxims of causal reasoning and induction. This essay reviews their significance for Newton’s method of inquiry, as well as their application to particular propositions within the Principia. Two main claims emerge. First, the rules are not only interrelated, they defend various facets of the same core idea: that nature is simple and orderly by divine decree, and that, consequently, human beings can be justified in inferring universal causes from limited phenomena, if (...)
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  15. How Effects Depend on Their Causes, Why Causal Transitivity Fails, and Why We Care About Causation.Gunnar Björnsson - 2007 - Philosophical Studies 133 (3):349-390.
    Despite recent efforts to improve on counterfactual theories of causation, failures to explain how effects depend on their causes are still manifest in a variety of cases. In particular, theories that do a decent job explaining cases of causal preemption have problems accounting for cases of causal intransitivity. Moreover, the increasing complexity of the counterfactual accounts makes it difficult to see why the concept of causation would be such a central part of our cognition. In this paper, I propose an (...)
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  16. Contrastivism in Philosophy: New Perspectives.Martijn Blaauw (ed.) - 2012 - Routledge.
    Contrastivism can be applied to a variety of problems within philosophy, and as such, it can be coherently seen as a unified movement. This volume brings together state-of-the-art research on the contrastive treatment of philosophical concepts and questions, including knowledge, belief, free will, moral luck, Bayesian confirmation theory, causation, and explanation.
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  17. 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.
    Since the late nineties there has been an increased interested in probabilistic logic learning, an area within AI that combines machine learning with logic-based knowledge representation and uncertainty reasoning. Several different formalisms for combining first-order logic with probability reasoning have been proposed, and it has been studied how models in these formalisms can be automatically learned from data. -/- This talk starts with a brief introduction to probabilistic logic learning, after which we will focus on a relatively new formalism known (...)
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  18. The Ways of the Wise: Hume's Rules of Causal Reasoning.Deborah Boyle - 2012 - Hume Studies 38 (2):157-182.
    In Hume’s own day, and for nearly two hundred years after that, readers interested in his account of causal reasoning tended to focus on the skeptical implications of that account. For example, in his 1757 View of the Principal Deistical Writers of the Last and Present Century, John Leland characterized Hume as “endeavouring to destroy all reasoning, from causes to effects, or from effects to causes.”1 According to this sort of reading, as Louis Loeb describes it, “there is equal justification (...)
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  19. Aggregating Causal Judgments.Richard Bradley, Franz Dietrich & Christian List - 2014 - Philosophy of Science 81 (4):491-515.
    Decision-making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals' causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments, and identify the limitations of this approach. We then explore the possibility of aggregating causal judgments independently of probabilistic ones. Formally, we introduce the problem of causal-network aggregation. (...)
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  20. 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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  21. 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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  22. 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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  23. 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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  24. 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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  25. Alexander Gebharter: Causal Nets, Interventionism, and Mechanisms. Philosophical Foundations and Applications.Lorenzo Casini - 2018 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 49 (3):481-485.
  26. 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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  27. 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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  28. 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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  29. 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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  30. 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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  31. Demoralizing Causation.David Danks, David Rose & Edouard Machery - 2013 - Philosophical Studies (2):1-27.
    There have recently been a number of strong claims that normative considerations, broadly construed, influence many philosophically important folk concepts and perhaps are even a constitutive component of various cognitive processes. Many such claims have been made about the influence of such factors on our folk notion of causation. In this paper, we argue that the strong claims found in the recent literature on causal cognition are overstated, as they are based on one narrow type of data about a particular (...)
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  32. 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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  33. 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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  34. 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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  35. Causal Reasoning, Causal Probabilities, and Conceptions of Causation.Isabelle Drouet - 2012 - Studies in History and Philosophy of Science Part C 43 (4):761-768.
    The present paper deals with the tools that can be used to represent causation and to reason about it and, specifically, with their diversity. It focuses on so-called “causal probabilities”—that is, probabilities of effects given one of their causes—and critically surveys a recent paper in which Joyce argues that the values of these probabilities do not depend on one’s conception of causation. I first establish a stronger independence claim: I show that the very definition of causal probabilities is independent of (...)
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  36. 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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  37. 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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  38. 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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  39. Interventions and Causal Inference.Frederick Eberhardt & Richard Scheines - 2007 - Philosophy of Science 74 (5):981-995.
    The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard' and ‘soft' interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the (...)
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  40. How Ethical Behavior of Firms is Influenced by the Legal and Political Environments: A Bayesian Causal Map Analysis Based on Stages of Development. [REVIEW]Ahmet Ekici & Sule Onsel - 2013 - Journal of Business Ethics 115 (2):271-290.
    Even though potential impacts of political and legal environments of business on ethical behavior of firms (EBOF) have been conceptually recognized, not much evidence (i.e., empirical work) has been produced to clarify their role. In this paper, using Bayesian causal maps (BCMs) methodology, relationships between legal and political environments of business and EBOF are investigated. The unique design of our study allows us to analyze these relationships based on the stages of development in 92 countries around the world. The EBOF (...)
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  41. 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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  42. 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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  43. The Frugal Inference of Causal Relations.Malcolm Forster, Garvesh Raskutti, Reuben Stern & Naftali Weinberger - 2018 - British Journal for the Philosophy of Science 69 (3):821-848.
    Recent approaches to causal modelling rely upon the causal Markov condition, which specifies which probability distributions are compatible with a directed acyclic graph. Further principles are required in order to choose among the large number of DAGs compatible with a given probability distribution. Here we present a principle that we call frugality. This principle tells one to choose the DAG with the fewest causal arrows. We argue that frugality has several desirable properties compared to the other principles that have been (...)
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  44. '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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  45. Causal Reasoning in Physics.Mathias Frisch - unknown
    In this paper I examine several neo-Russellian arguments for the claim that there is no room for an asymmetric notion of cause in mature physical theories. I argue that these arguments are unsuccessful and discuss an example where an asymmetric causal condition plays an important role in the derivation of a physical law.
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  46. 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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  47. Uncovering Constitutive Relevance Relations in Mechanisms.Alexander Gebharter - 2017 - Philosophical Studies 174 (11):2645-2666.
    In this paper I argue that constitutive relevance relations in mechanisms behave like a special kind of causal relation in at least one important respect: Under suitable circumstances constitutive relevance relations produce the Markov factorization. Based on this observation one may wonder whether standard methods for causal discovery could be fruitfully applied to uncover constitutive relevance relations. This paper is intended as a first step into this new area of philosophical research. I investigate to what extent the PC algorithm, originally (...)
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  48. Causal Nets, Interventionism, and Mechanisms: Philosophical Foundations and Applications.Alexander Gebharter - 2017 - Cham: Springer.
    This monograph looks at causal nets from a philosophical point of view. The author shows that one can build a general philosophical theory of causation on the basis of the causal nets framework that can be fruitfully used to shed new light on philosophical issues. Coverage includes both a theoretical as well as application-oriented approach to the subject. The author first counters David Hume’s challenge about whether causation is something ontologically real. The idea behind this is that good metaphysical concepts (...)
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  49. Causal Exclusion and Causal Bayes Nets.Alexander Gebharter - 2017 - Philosophy and Phenomenological Research 95 (2):353-375.
    In this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments within the theory of causal Bayes nets. I argue that supervenience relations formally behave like causal relations. If this is correct, then it turns out that both versions of the exclusion argument are valid when assuming the causal Markov condition and the causal minimality condition. I also investigate some consequences for the recent discussion of causal exclusion arguments in the light of an interventionist theory (...)
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  50. Another Problem with RBN Models of Mechanisms.Alexander Gebharter - 2016 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 31 (2):177-188.
    Casini, Illari, Russo, and Williamson (2011) suggest to model mechanisms by means of recursive Bayesian networks (RBNs) and Clarke, Leuridan, and Williamson (2014) extend their modelling approach to mechanisms featuring causal feedback. One of the main selling points of the RBN approach should be that it provides answers to questions concerning manipulation and control. In this paper I demonstrate that the method to compute the effects of interventions the authors mentioned endorse leads to absurd results under the additional assumption of (...)
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