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Clark Glymour [207]Clark N. Glymour [5]Clark Noren Glymour [1]
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Clark Glymour
Carnegie Mellon University
  1. Theory and Evidence.Clark Glymour - 1980 - Princeton University Press.
  2. Causation, Prediction, and Search.Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
  3.  59
    A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.Alison Gopnik, Clark Glymour, Laura Schulz, Tamar Kushnir & David Danks - 2002 - Psychological Review 111 (1):3-32.
    We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...)
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  4.  98
    Theoretical Equivalence and the Semantic View of Theories.Clark Glymour - 2013 - Philosophy of Science 80 (2):286-297.
  5.  5
    Thinking Things Through.Clark Glymour - unknown
    A Photcopy of Thinking Things Through, Princeton Univeresity Press, 1980.
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  6. Actual Causation: A Stone Soup Essay.Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey & Richard Scheines - 2010 - Synthese 175 (2):169-192.
    We argue that current discussions of criteria for actual causation are ill-posed in several respects. (1) The methodology of current discussions is by induction from intuitions about an infinitesimal fraction of the possible examples and counterexamples; (2) cases with larger numbers of causes generate novel puzzles; (3) "neuron" and causal Bayes net diagrams are, as deployed in discussions of actual causation, almost always ambiguous; (4) actual causation is (intuitively) relative to an initial system state since state changes are relevant, but (...)
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  7. Causation, Prediction, and Search.Peter Spirtes, Clark Glymour, Scheines N. & Richard - 2000 - Mit Press: Cambridge.
  8.  11
    The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology.Clark Glymour - 2003 - Erkenntnis 59 (1):136-140.
  9.  12
    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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  10.  49
    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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  11.  26
    Clark Glymour’s Responses to the Contributions to the Synthese Special Issue “Causation, Probability, and Truth: The Philosophy of Clark Glymour”.Clark Glymour - 2016 - Synthese 193 (4):1251-1285.
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  12. James Woodward, Making Things Happen: A Theory of Causal Explanation, Oxford, 2003, 418pp, $65.00 (Hbk) ISBN 0195155270. [REVIEW]Clark Glymour - 2004 - British Journal for the Philosophy of Science 55 (4):779-790.
    "Goodness of Fit": Clinical Applications from Infancy through Adult Life. By Stella Chess & Alexander Thomas. Brunner/Mazel, Philadelphia, PA, 1999. pp. 229. pound24.95 (hb). Chess and Thomas's pioneering longitudinal studies of temperamental individuality started over 40 years ago (Thomas et al., 1963). Their publications soon became and remain classics. Their concept of "goodness of fit" emerges out of this monumental work but has had a long gestation period. In their new book, the authors distinguish between behaviour disorders that are reactive (...)
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  13.  47
    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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  14.  73
    Probability and the Explanatory Virtues: Figure 1.Clark Glymour - 2015 - British Journal for the Philosophy of Science 66 (3):591-604.
    Recent literature in philosophy of science has addressed purported notions of explanatory virtues—‘explanatory power’, ‘unification’, and ‘coherence’. In each case, a probabilistic relation between a theory and data is said to measure the power of an explanation, or degree of unification, or degree of coherence. This essay argues that the measures do not capture cases that are paradigms of scientific explanation, that the available psychological evidence indicates that the measures do not capture judgements of explanatory power, and, finally, that the (...)
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  15. Theory and Evidence.Clark Glymour - 1980 - Philosophy of Science 48 (3):498-500.
     
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  16. Conditioning and Intervening.Christopher Meek & Clark Glymour - 1994 - British Journal for the Philosophy of Science 45 (4):1001-1021.
    We consider the dispute between causal decision theorists and evidential decision theorists over Newcomb-like problems. We introduce a framework relating causation and directed graphs developed by Spirtes et al. (1993) and evaluate several arguments in this context. We argue that much of the debate between the two camps is misplaced; the disputes turn on the distinction between conditioning on an event E as against conditioning on an event I which is an action to bring about E. We give the essential (...)
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  17.  9
    Creative Abduction, Factor Analysis, and the Causes of Liberal Democracy.Clark Glymour - 2018 - Kriterion - Journal of Philosophy.
    The ultimate focus of the current essay is on methods of “creative abduction” that have some guarantees as reliable guides to the truth, and those that do not. Emphasizing work by Richard Englehart using data from the World Values Survey, Gerhard Schurz has analyzed literature surrounding Samuel Huntington’s well-known claims that civilization is divided into eight contending traditions, some of which resist “modernization” – democracy, civil rights, equality of rights of women and minorities, secularism. Schurz suggests an evolutionary model of (...)
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  18.  7
    The Evaluation of Discovery: Models, Simulation and Search Through “Big Data”.Kun Zhang, Joseph D. Ramsey & Clark Glymour - 2019 - Open Philosophy 2 (1):39-48.
    A central theme in western philosophy was to find formal methods that can reliably discover empirical relationships and their explanations from data assembled from experience. As a philosophical project, that ambition was abandoned in the 20th century and generally dismissed as impossible. It was replaced in philosophy by neo-Kantian efforts at reconstruction and justification, and in professional statistics by the more limited ambition to estimate a small number of parameters in pre-specified hypotheses. The influx of “big data” from climate science, (...)
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  19. The Epistemology of Geometry.Clark Glymour - 1977 - Noûs 11 (3):227-251.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  20.  19
    Hans Reichenbach.Clark Glymour - 2008 - Stanford Encyclopedia of Philosophy.
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  21.  95
    Learning, Prediction and Causal Bayes Nets.Clark Glymour - 2003 - Trends in Cognitive Sciences 7 (1):43-48.
  22.  21
    Causal Maps and Bayes Nets: A Cognitive and Computational Account of Theory-Formation.Alison Gopnik & Clark Glymour - 2002 - In Peter Carruthers, Stephen P. Stich & Michael Siegal (eds.), The Cognitive Basis of Science. Cambridge University Press. pp. 117--132.
  23. Determinism, Ignorance, and Quantum Mechanics.Clark Glymour - 1971 - Journal of Philosophy 68 (21):744-751.
    is every bit as intelligible and philosophically respectable as many other doctrines currently in favor, e.g., the doctrine that mental events are identical with brain events; the attempt to give a linguistic construal of this latter doctrine meets many of the same sorts of difficulties encountered above (see Hempel, op. cit.). Secondly, I think that evidence for universal determinism may not, as a matter of fact, be so hard to come by as one might imagine. It is a striking fact (...)
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  24. A Semantics and Methodology for Ceteris Paribus Hypotheses.Clark Glymour - 2002 - Erkenntnis 57 (3):395-405.
    Taking seriously the arguments of Earman, Roberts and Smith that ceteris paribus laws have no semantics and cannot be tested, I suggest that ceteris paribus claims have a kind of formal pragmatics, and that at least some of them can be verified or refuted in the limit.
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  25.  51
    Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition.Joseph Ramsey, Peter Spirtes & Clark Glymour - unknown
    Reflectance spectroscopy is a standard tool for studying the mineral composition of rock and soil samples and for remote sensing of terrestrial and extraterrestrial surfaces. We describe research on automated methods of mineral identification from reflectance spectra and give evidence that a simple algorithm, adapted from a well-known search procedure for Bayes nets, identifies the most frequently occurring classes of carbonates with reliability equal to or greater than that of human experts. We compare the reliability of the procedure to the (...)
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  26. 5. Markov Properties and Quantum Experiments.Clark Glymour - unknown
    Few people have thought so hard about the nature of the quantum theory as has Jeff Bub,· and so it seems appropriate to offer in his honor some reflections on that theory. My topic is an old one, the consistency of our microscopic theories with our macroscopic theories, my example, the Aspect experiments (Aspect et al., 1981, 1982, 1982a; Clauser and Shimony, l978;_Duncan and Kleinpoppen, 199,8) is familiar, and my sirnplrcation of it is borrowed. All that is new here is (...)
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  27. Reasons as Causes in Bayesian Epistemology.Clark Glymour & David Danks - 2007 - Journal of Philosophy 104 (9):464-474.
    In everyday matters, as well as in law, we allow that someone’s reasons can be causes of her actions, and often are. That correct reasoning accords with Bayesian principles is now so widely held in philosophy, psychology, computer science and elsewhere that the contrary is beginning to seem obtuse, or at best quaint. And that rational agents should learn about the world from energies striking sensory inputs nerves in people—seems beyond question. Even rats seem to recognize the difference between correlation (...)
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  28.  83
    Reverse Inference in Neuropsychology.Clark Glymour & Catherine Hanson - 2016 - British Journal for the Philosophy of Science 67 (4):1139-1153.
    Reverse inference in cognitive neuropsychology has been characterized as inference to ‘psychological processes’ from ‘patterns of activation’ revealed by functional magnetic resonance or other scanning techniques. Several arguments have been provided against the possibility. Focusing on Machery’s presentation, we attempt to clarify the issues, rebut the impossibility arguments, and propose and illustrate a strategy for reverse inference. 1 The Problem of Reverse Inference in Cognitive Neuropsychology2 The Arguments2.1 The anti-Bayesian argument3 Patterns of Activation4 Reverse Inference Practiced5 Seek and Ye Shall (...)
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  29.  28
    Einstein and Hilbert: Two Months in the History of General Relativity.John Earman & Clark Glymour - unknown
  30.  4
    Theory and Evidence.Isaac Levi & Clark Glymour - 1982 - Philosophical Review 91 (1):124.
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  31. 3 Actual Causes and Thought Experiments.Clark Glymour & Frank Wimberly - 2007 - In J. K. Campbell, M. O'Rourke & H. S. Silverstein (eds.), Causation and Explanation. MIT Press. pp. 4--43.
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  32.  2
    The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology.Clark Glymour - 2003 - Mind 112 (446):340-343.
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  33.  87
    Causal Mechanism and Probability: A Normative Approach.Clark Glymour - unknown
    & Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic (...)
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  34.  54
    Foundations of Space-Time Theories: Minnesota Studies in the Philosophy of Science.John Earman, Clark Glymour & John Stachel (eds.) - 1977 - University of Minnesota Press.
    Some Philosophical Prehistory of General Relativity As history, my remarks will form rather a medley. If they can claim any sort of unity (apart from a ...
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  35. 10. Interpreting Quantum Field Theory Interpreting Quantum Field Theory (Pp. 348-378).Michael Friedman, Robert DiSalle, J. D. Trout, Shaun Nichols, Maralee Harrell, Clark Glymour, Carl G. Wagner, Kent W. Staley, Jesús P. Zamora Bonilla & Frederick M. Kronz - 2002 - Philosophy of Science 69 (2).
     
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  36.  52
    When is a Brain Like the Planet?Clark Glymour - 2007 - Philosophy of Science 74 (3):330-347.
    Time series of macroscopic quantities that are aggregates of microscopic quantities, with unknown one‐many relations between macroscopic and microscopic states, are common in applied sciences, from economics to climate studies. When such time series of macroscopic quantities are claimed to be causal, the causal relations postulated are representable by a directed acyclic graph and associated probability distribution—sometimes called a dynamical Bayes net. Causal interpretations of such series imply claims that hypothetical manipulations of macroscopic variables have unambiguous effects on variables “downstream” (...)
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  37.  14
    Learning the Structure of Deterministic Systems.Clark Glymour - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 231--240.
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  38.  9
    James Woodward, Making Things Happen: A Theory of Causal Explanation, Oxford, 2003, 418pp, &Dollar;65.00 ISBN 0195155270. [REVIEW]Clark Glymour - 2004 - British Journal for the Philosophy of Science 55 (4):779-790.
  39. Why I Am Not a Bayesian.Clark Glymour - 2010 - In Antony Eagle (ed.), Philosophy of Probability: Contemporary Readings. Routledge.
     
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  40.  36
    Editorial.John Earman, Clark Glymour & Sandra Mitchell - 2002 - Erkenntnis 57 (3):277-280.
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  41.  65
    On the Methods of Cognitive Neuropsychology.Clark Glymour - 1994 - British Journal for the Philosophy of Science 45 (3):815-35.
    Contemporary cognitive neuropsychology attempts to infer unobserved features of normal human cognition, or ?cognitive architecture?, from experiments with normals and with brain-damaged subjects in whom certain normal cognitive capacities are altered, diminished, or absent. Fundamental methodological issues about the enterprise of cognitive neuropsychology concern the characterization of methods by which features of normal cognitive architecture can be identified from such data, the assumptions upon which the reliability of such methods are premised, and the limits of such methods?even granting their assumptions?in (...)
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  42.  18
    Relativity and Eclipses: The British Eclipse Expedition of 1919 and its Predecessors.John Earman & Clark Glymour - unknown
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  43. The Gravitational Red Shift as a Test of General Relativity: History and Analysis.John Earman & Clark Glymour - 1980 - Studies in History and Philosophy of Science Part A 11 (3):175-214.
  44.  30
    Indistinguishable Space-Times and the Fundamental Group.Clark Glymour - unknown
  45.  41
    Theoretical Realism and Theoretical Equivalence.Clark Glymour - 1970 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1970:275 - 288.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/tenns.htm1. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  46. Instrumental Probability.Clark Glymour - 2001 - The Monist 84 (2):284-300.
    The claims of science and the claims of probability combine in two ways. In one, probability is part of the content of science, as in statistical mechanics and quantum theory and an enormous range of "models" developed in applied statistics. In the other, probability is the tool used to explain and to justify methods of inference from records of observations, as in every science from psychiatry to physics. These intimacies between science and probability are logical sports, for while we think (...)
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  47.  53
    Hypothetico-Deductivism is Hopeless.Clark Glymour - 1980 - Philosophy of Science 47 (2):322-325.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  48.  85
    What is Right with 'Bayes Net Methods' and What is Wrong with 'Hunting Causes and Using Them'?Clark Glymour - 2010 - British Journal for the Philosophy of Science 61 (1):161-211.
    Nancy Cartwright's recent criticisms of efforts and methods to obtain causal information from sample data using automated search are considered. In addition to reviewing that effort, I argue that almost all of her criticisms are false and rest on misreading, overgeneralization, or neglect of the relevant literature.
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  49.  20
    What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models.Alexander Murray-Watters & Clark Glymour - 2015 - Philosophy of Science 82 (4):556-586.
    Using Gebharter’s representation, we consider aspects of the problem of discovering the structure of unmeasured submechanisms when the variables in those submechanisms have not been measured. Exploiting an early insight of Sober’s, we provide a correct algorithm for identifying latent, endogenous structure—submechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned.
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  50.  47
    Lost in the Tensors: Einstein's Struggles with Covariance Principles 1912–1916.John Earman & Clark Glymour - 1978 - Studies in History and Philosophy of Science Part A 9 (4):251-278.
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