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Clark Glymour [202]Clark N. Glymour [3]Clark Noren Glymour [1]
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Profile: Clark Glymour (Carnegie Mellon University)
  1.  32
    Clark Glymour (1980). Theory and Evidence. Princeton University Press.
  2. Peter Spirtes, Clark Glymour & Richard Scheines (1996). Causation, Prediction, and Search. British Journal for the Philosophy of Science 47 (1):113-123.
     
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  3.  36
    Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir & David Danks, A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
    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.  52
    Clark Glymour (2013). Theoretical Equivalence and the Semantic View of Theories. Philosophy of Science 80 (2):286-297.
  5. Clark Glymour (1971). Determinism, Ignorance, and Quantum Mechanics. 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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  6. Peter Spirtes, Clark Glymour, Scheines N. & Richard (2000). Causation, Prediction, and Search. MIT Press: Cambridge.
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  7.  82
    Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng & Jiji Zhang (2010). Actual Causation: A Stone Soup Essay. 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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  8. Clark Glymour & Frank Wimberly (2007). 3 Actual Causes and Thought Experiments. In J. K. Campbell, M. O'Rourke & H. S. Silverstein (eds.), Causation and Explanation. MIT Press 4--43.
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  9.  2
    Clark Glymour, Alison Gopnik, David M. Sobel & Laura E. Schulz, Causal Learning Mechanisms in Very Young Children: Two-, Three-, and Four-Year-Olds Infer Causal Relations From Patterns of Variation and Covariation.
  10.  49
    Clark Glymour (2015). Probability and the Explanatory Virtues. 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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  11. Clark Glymour (2003). The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology. Erkenntnis 59 (1):136-140.
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  12.  10
    Clark Glymour, Richard Scheines, Peter Spirtes & Kevin T. Kelly, Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
    Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
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  13. Christopher Meek & Clark Glymour (1994). Conditioning and Intervening. 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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  14.  5
    Clark Glymour, Hans Reichenbach. Stanford Encyclopedia of Philosophy.
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  15. Clark Glymour (2002). A Semantics and Methodology for Ceteris Paribus Hypotheses. 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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  16.  6
    Clark Glymour (2016). Clark Glymour’s Responses to the Contributions to the Synthese Special Issue “Causation, Probability, and Truth: The Philosophy of Clark Glymour”. Synthese 193 (4):1251-1285.
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  17.  26
    Clark Glymour (1998). Learning Causes: Psychological Explanations of Causal Explanation. [REVIEW] 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 (...)
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  18. Clark Glymour, 5. Markov Properties and Quantum Experiments.
    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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  19.  11
    Alison Gopnik & Clark Glymour (2002). Causal Maps and Bayes Nets: A Cognitive and Computational Account of Theory-Formation. In Peter Carruthers, Stephen P. Stich & Michael Siegal (eds.), The Cognitive Basis of Science. Cambridge University Press 117--132.
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  20.  48
    Clark Glymour (2003). Learning, Prediction and Causal Bayes Nets. Trends in Cognitive Sciences 7 (1):43-48.
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  21.  3
    Clark Glymour (2007). Learning the Structure of Deterministic Systems. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press 231--240.
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  22.  35
    John Earman, Clark Glymour & John Stachel (eds.) (1977). Foundations of Space-Time Theories: Minnesota Studies in the Philosophy of Science. 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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  23.  22
    Clark Glymour, Indistinguishable Space-Times and the Fundamental Group.
  24. 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). 10. Interpreting Quantum Field Theory Interpreting Quantum Field Theory (Pp. 348-378). Philosophy of Science 69 (2).
     
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  25.  11
    Clark Glymour (2004). Critical Notice of Making Things Happen. British Journal for the Philosophy of Science 55:779-790.
    "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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  26.  3
    John Earman & Clark Glymour, Einstein and Hilbert: Two Months in the History of General Relativity.
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  27.  67
    Clark Glymour (2010). What is Right with 'Bayes Net Methods' and What is Wrong with 'Hunting Causes and Using Them'? 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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  28.  14
    Alexander Murray-Watters & Clark Glymour (2015). What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models. 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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  29.  21
    John Earman, Clark Glymour & Sandra Mitchell (2002). Editorial. Erkenntnis 57 (3):277-280.
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  30.  7
    Clark Glymour (2014). Poincaré’s Probabilities, Kantified, Post-Modernized. Biological Theory 9 (1):113-114.
  31.  42
    Clark Glymour (1977). The Epistemology of Geometry. Noûs 11 (3):227-251.
  32.  12
    Clark Glymour, Review of James Woodward, Making Things Happen: A Theory of Causal Explanation. [REVIEW]
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  33. Clark Glymour (1977). The Sum Rule is Well-Confirmed. Philosophy of Science 44 (1):86-94.
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  34.  7
    Joseph Ramsey, Peter Spirtes & Clark Glymour, Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition.
    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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  35.  60
    Clark Glymour, Causal Mechanism and Probability: A Normative Approach.
    & 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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  36.  5
    John Earman & Clark Glymour, Relativity and Eclipses: The British Eclipse Expedition of 1919 and its Predecessors.
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  37.  25
    Clark Glymour (2007). When is a Brain Like the Planet? 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 (...)
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  38.  85
    Kevin T. Kelly & Clark Glymour (1989). Convergence to the Truth and Nothing but the Truth. Philosophy of Science 56 (2):185-220.
    One construal of convergent realism is that for each clear question, scientific inquiry eventually answers it. In this paper we adapt the techniques of formal learning theory to determine in a precise manner the circumstances under which this ideal is achievable. In particular, we define two criteria of convergence to the truth on the basis of evidence. The first, which we call EA convergence, demands that the theorist converge to the complete truth "all at once". The second, which we call (...)
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  39. Clark Glymour (1981). Correction. Journal of Philosophy 78 (1):58 -.
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  40.  66
    John Earman & Clark Glymour (1988). What Revisions Does Bootstrap Testing Need? A Reply. Philosophy of Science 55 (2):260-264.
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  41. Peter Spirtes, Clark Glymour & Richard Scheines (1997). Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search. British Journal for the Philosophy of Science 48 (4):555-568.
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  42.  56
    John Earman & Clark Glymour (1980). The Gravitational Red Shift as a Test of General Relativity: History and Analysis. Studies in History and Philosophy of Science Part A 11 (3):175-214.
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  43. Clark Glymour (2010). Why I Am Not a Bayesian. In Antony Eagle (ed.), Philosophy of Probability: Contemporary Readings. Routledge
     
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  44.  11
    Clark Glymour (1992). Android Epistemology: Computation, Artificial Intelligence. In Merrilee H. Salmon (ed.), Introduction to the Philosophy of Science. Hackett Pub. 364.
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  45.  16
    John Earman & Clark Glymour (1978). Lost in the Tensors: Einstein's Struggles with Covariance Principles 1912–1916. Studies in History and Philosophy of Science Part A 9 (4):251-278.
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  46.  58
    Clark Glymour (1975). Relevant Evidence. Journal of Philosophy 72 (14):403-426.
    S CIENTISTS often claim that an experiment or observation tests certain hypotheses within a complex theory but not others. Relativity theorists, for example, are unanimous in the judgment that measurements of the gravitational red shift do not test the field equations of general relativity; psychoanalysts sometimes complain that experimental tests of Freudian theory are at best tests of rather peripheral hypotheses; astronomers do not regard observations of the positions of a single planet as a test of Kepler's third law, even (...)
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  47.  8
    Clark Glymour (1987). ESP and the Big Stuff. Behavioral and Brain Sciences 10 (4):590.
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  48.  10
    Frederick Eberhardt & Clark Glymour (2004). Hans Reichenbach's Probability Logic. In Dov M. Gabbay, John Woods & Akihiro Kanamori (eds.), Handbook of the History of Logic. Elsevier 10--357.
  49.  10
    Herbert A. Simon, Discovering Explanations, Clark Glymour, Andy Clark, Twisted Tales, Alison Gopnik & Explanation as Orgasm (1998). Cognition and Explanation. Cognition 8 (1).
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  50.  30
    Clark Glymour (1999). Rabbit Hunting. Synthese 121 (1-2):55-78.
    Twenty years ago, Nancy Cartwright wrote a perceptive essay in which she clearly distinguished causal relations from associations, introduced philosophers to Simpson’s paradox, articulated the difficulties for reductive probabilistic analyses of causation that flow from these observations, and connected causal relations with strategies of action (Cartwright 1979). Five years later, without appreciating her essay, I and my (then) students began to develop formal representations of causal and probabilistic relations, which, subsequently informed by the work of computer scientists and statisticians, led (...)
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