Results for 'Richard Scheines Joseph Ramsey'

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  1. 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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    Simulating Genetic Regulartory Networks.Richard Scheines & Joe Ramsey - unknown
    Richard Scheines and Joe Ramsey. Simulating Genetic Regulartory Networks.
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    Time and Attention: Students, Sessions, and Tasks.Andrew Arnold, Richard Scheines, Joseph E. Back & Bill Jerome - unknown
    Students in two classes in the fall of 2004 making extensive use of online courseware were logged as they visited over 500 different “learning pages” which varied in length and in difficulty. We computed the time spent on each page by each student during each session they were logged in. We then modeled the time spent for a particular visit as a function of the page itself, the session, and the student. Surprisingly, the average time a student spent on learning (...)
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  4.  18
    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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  5.  15
    Single Mother's Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study.Aurora P. Jackson & Richard Scheines - unknown
    Aurora P. Jackson and Richard Scheines. Single Mother's Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study.
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  6.  15
    Computer Environments for Proof Construction.Richard Scheines & Wilfried Sieg - unknown
    Richard Scheines and Wilfred Sieg. Computer Environments for Proof Construction.
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  7.  6
    Learning Linear Causal Structure Equation Models with Genetic Algorithms.Shane Harwood & Richard Scheines - unknown
    Shane Harwood and Richard Scheines. Learning Linear Causal Structure Equation Models with Genetic Algorithms.
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  8.  10
    The Limits of Causal Knowledge.James M. Robins, Richard Scheines, Peter Spirtes & Larry Wasserman - unknown
    James M. Robins, Richard Scheines, Peter Spirtes, and Larry Wasserman. The Limits of Causal Knowledge.
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  9.  10
    Regression and Causation.Clark Glymour, Richard Scheines, Peter Spirtes & Christopher Meek - unknown
    Clark Glymour, Richard Scheines, Peter Spirtes, and Christopher Meek. Regression and Causation.
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  10.  4
    Genetic Algorithm Search Over Causal Models.Shane Harwood & Richard Scheines - unknown
    Shane Harwood and Richard Scheines. Genetic Algorithm Search Over Causal Models.
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  11.  9
    Causal Inference of Ambiguous Manipulations.Peter Spirtes & Richard Scheines - 2004 - Philosophy of Science 71 (5):833-845.
    Peter Spirtes and Richard Scheines. Causal Inference and Ambiguous Manipulations.
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  12.  4
    Prediction and Experimental Design with Graphical Causal Models.Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, S. Fineberg & E. Slate - unknown
    Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, S. Fineberg, E. Slate. Prediction and Experimental Design with Graphical Causal Models.
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  13.  7
    Teaching and Learning with Online Courses.Richard Scheines, Gaea Leinhardt, Joel Smith & Kwangsu Cho - unknown
    Richard Scheines, Gaea Leinhardt, Joel Smith, and Kwangsu Cho. Teaching and Learning with Online Courses.
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  14.  1
    Simulated Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD, EQS and LISREL Programs.Peter Spirtes, Richard Scheines & Clark Glymour - unknown
    Peter Spirtes, Richard Scheines and Clark Glymour. Simulated Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD, EQS and LISREL Programs.
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  15.  2
    A REPLY TO RICHARD McCORMICK: The Enforcement of Morals: Nontherapeutic Research on Children.Paul Ramsey - 1976 - Hastings Center Report 6 (4):21-30.
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  16.  42
    Review of Richard Menary (Ed.), The Extended Mind[REVIEW]William Ramsey - 2010 - Notre Dame Philosophical Reviews 2010 (12).
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  17.  1
    COPPENS, Joseph, éd., La notion biblique de Dieu. Le Dieu de la Bible et le Dieu des philosophes.Jean Richard - 1978 - Laval Théologique et Philosophique 34 (3):324-332.
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  18. And Joseph Garon.William Ramsey & Stephen Stich - 1995 - In Cynthia Macdonald & Graham Macdonald (eds.), Connectionism: Debates on Psychological Explanation. Blackwell. pp. 311.
     
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  19. Nine Modern Moralists Paul Tillich, Karl Marx, H. Richard Niebuhr, Fyodor Dostoevski, Reinhold Niebuhr, Jacques Maritain, Jean-Paul Sartre, Emil Brunner, Edmond Cahn.Paul Ramsey - 1983
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  20.  8
    Causality From Probability.Peter Spirtes, Clark Glymour & Rcihard Scheines - unknown
    Peter Spirtes, Clark Glymour and Richard Scheines. Causality From Probability.
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  21.  43
    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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  22.  7
    Bootstrapping the PC and CPC Algorithms to Improve Search Accuracy.Joseph Ramsey - unknown
    By bootstrapping the output of the PC algorithm (Spirtes et al., 2000; Meek 1995), using larger conditioning sets informed by the current graph state, it is possible to define a novel algorithm, JPC, that improves accuracy of search for i.i.d. data drawn from linear, Gaussian, sparse to moderately dense models. The motivation for constructing sepsets using information in the current graph state is to highlight the differences between d-­‐separation information in the graph and conditional independence information extracted from the sample. (...)
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  23. Causation, Prediction, and Search.Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
  24. Philosophical Temperament.Jonathan Livengood, Justin Sytsma, Adam Feltz, Richard Scheines & Edouard Machery - 2010 - Philosophical Psychology 23 (3):313-330.
    Many philosophers have worried about what philosophy is. Often they have looked for answers by considering what it is that philosophers do. Given the diversity of topics and methods found in philosophy, however, we propose a different approach. In this article we consider the philosophical temperament, asking an alternative question: what are philosophers like? Our answer is that one important aspect of the philosophical temperament is that philosophers are especially reflective: they are less likely than their peers to embrace what (...)
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  25. Coherence and Confirmation Through Causation.Gregory Wheeler & Richard Scheines - 2013 - Mind 122 (485):135-170.
    Coherentism maintains that coherent beliefs are more likely to be true than incoherent beliefs, and that coherent evidence provides more confirmation of a hypothesis when the evidence is made coherent by the explanation provided by that hypothesis. Although probabilistic models of credence ought to be well-suited to justifying such claims, negative results from Bayesian epistemology have suggested otherwise. In this essay we argue that the connection between coherence and confirmation should be understood as a relation mediated by the causal relationships (...)
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  26.  59
    Connectionism, Eliminativism and the Future of Folk Psychology.William Ramsey, Stephen Stich & Joseph Garon - 1990 - Philosophical Perspectives 4:499-533.
  27.  47
    Causal Inference of Ambiguous Manipulations.Peter Spirtes & Richard Scheines - 2004 - Philosophy of Science 71 (5):833-845.
    Over the last two decades, a fundamental outline of a theory of causal inference has emerged. However, this theory does not consider the following problem. Sometimes two or more measured variables are deterministic functions of one another, not deliberately, but because of redundant measurements. In these cases, manipulation of an observed defined variable may actually be an ambiguous description of a manipulation of some underlying variables, although the manipulator does not know that this is the case. In this article we (...)
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  28. Evaluation of a Behavioral Measure of Risk Taking: The Balloon Analogue Risk Task.C. W. Lejuez, Jennifer P. Read, Christopher W. Kahler, Jerry B. Richards, Susan E. Ramsey, Gregory L. Stuart, David R. Strong & Richard A. Brown - 2002 - Journal of Experimental Psychology: Applied 8 (2):75-84.
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  29.  26
    Remembering Without Knowing.Keith Lehrer & Joseph Richard - 1975 - Grazer Philosophische Studien 1:121-126.
    Memory sometimes yields knowledge and sometimes does not. It is, however, natural to suppose that i f a man remembers that p, then he knows that p and formerly knew that p. Remembering something is plausibly construed as a f o rm of knowing something which one has not forgotten and which one knew previously. We argue, to the contrary, that this thesis is false. We present four counterexamples to the thesis that support a different analysis of remembering. We propose (...)
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  30.  71
    Causation, Association and Confirmation.Gregory Wheeler & Richard Scheines - 2011 - In Stephan Hartmann, Marcel Weber, Wenceslao Gonzalez, Dennis Dieks & Thomas Uebe (eds.), Explanation, Prediction, and Confirmation: New Trends and Old Ones Reconsidered. Springer. pp. 37--51.
    Many philosophers of science have argued that a set of evidence that is "coherent" confirms a hypothesis which explains such coherence. In this paper, we examine the relationships between probabilistic models of all three of these concepts: coherence, confirmation, and explanation. For coherence, we consider Shogenji's measure of association (deviation from independence). For confirmation, we consider several measures in the literature, and for explanation, we turn to Causal Bayes Nets and resort to causal structure and its constraint on probability. All (...)
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  31.  1
    The Donor is in the Details.Cynthia E. Cryder, George Loewenstein & Richard Scheines - unknown
    Recent research finds that people respond more generously to individual victims described in detail than to equivalent statistical victims described in general terms. We propose that this “identified victim effect” is one manifestation of a more general phenomenon: a positive influence of tangible information on generosity. In three experiments, we find evidence for an “identified intervention effect”; providing tangible details about a charity’s interventions significantly increases donations to that charity. Although previous work described sympathy as the primary mediator between tangible (...)
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  32.  7
    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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  33.  24
    The Similarity of Causal Inference in Experimental and Non-Experimental Studies.Richard Scheines - 2005 - Philosophy of Science 72 (5):927-940.
    For nearly as long as the word “correlation” has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect them to probabilistic relations. One strand of this work studies the conditions under which evidence (...)
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  34. Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search.Peter Spirtes, Clark Glymour & Richard Scheines - 1997 - British Journal for the Philosophy of Science 48 (4):555-568.
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  35.  39
    Independence Relations Produced by Parameter Values in Causal Models.Richard Scheines - 1990 - Philosophical Topics 18 (2):55-70.
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  36.  42
    An Introduction to Causal Inference.Richard Scheines - unknown
    In Causation, Prediction, and Search (CPS hereafter), Peter Spirtes, Clark Glymour and I developed a theory of statistical causal inference. In his presentation at the Notre Dame conference (and in his paper, this volume), Glymour discussed the assumptions on which this theory is built, traced some of the mathematical consequences of the assumptions, and pointed to situations in which the assumptions might fail. Nevertheless, many at Notre Dame found the theory difficult to understand and/or assess. As a result I was (...)
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  37. Doing Evil to Achieve Good Moral Choice in Conflict Situations.Richard A. Mccormick & Paul Ramsey - 1978
     
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  38.  38
    Causation, Truth, and the Law.Richard Scheines - unknown
    Deciding matters of legal liability, in torts and other civil actions, requires deciding causation. The injury suffered by a plaintiff must be caused by an event or condition due to the defendant. The courts distinguish between cause-in-fact and proximate causation, where cause-in-fact is determined by the “but-for” test: the effect would not have happened, “but for” the cause.1 Proximate causation is a set of legal limitations on cause-in-fact.
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  39.  19
    On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables.Richard Scheines - unknown
    vertices of a DAG. of K? We assume there are no unmeasured common causes of the N variables, that the system is free of feedback, and that the independence relations true of..
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  40.  27
    Combining Experiments to Discover Linear Cyclic Models.Richard Scheines - unknown
    We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relations are linear, but is otherwise completely general: It provides consistent estimates when the true causal structure contains feedback loops and latent variables, while the experiments can involve surgical or ‘soft’ interventions on one or multiple variables at a time. The algorithm is ‘online’ in the sense that it combines the results from (...)
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  41.  10
    N-1 Experiments Suffice to Determine the Causal Relations Among N Variables.Frederick Eberhardt, Clark Glymour & Richard Scheines - unknown
    By combining experimental interventions with search procedures for graphical causal models we show that under familiar assumptions, with perfect data, N - 1 experiments suffice to determine the causal relations among N > 2 variables when each experiment randomizes at most one variable. We show the same bound holds for adaptive learners, but does not hold for N > 4 when each experiment can simultaneously randomize more than one variable. This bound provides a type of ideal for the measure of (...)
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  42.  4
    Learning Measurement Models for Unobserved Variables.Ricardo Silva, Richard Scheines, Clark Glymour & Peter Spirtes - unknown
  43.  32
    From Probability to Causality.Peter Spirtes, Clark Glymour & Richard Scheines - 1991 - Philosophical Studies 64 (1):1 - 36.
  44.  54
    Causal Modeling with the TETRAD Program.Clark Glymour & Richard Scheines - 1986 - Synthese 68 (1):37 - 63.
    Drawing substantive conclusions from linear causal models that perform acceptably on statistical tests is unreasonable if it is not known how alternatives fare on these same tests. We describe a computer program, TETRAD, that helps to search rapidly for plausible alternatives to a given causal structure. The program is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence. We describe these principles, discuss how TETRAD employs them, and argue that these principles make TETRAD an effective tool. (...)
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  45. Universals and the "Method of Analysis".H. W. B. Joseph, F. P. Ramsey & R. B. Braithwaite - 1926 - Aristotelian Society Supplementary Volume 6:1-38.
     
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  46.  10
    A Response Time Model for Bottom-Out Hints as Worked Examples.Richard Scheines - unknown
    Students can use an educational system’s help in unexpected ways. For example, they may bypass abstract hints in search of a concrete solution. This behavior has traditionally been labeled as a form of gaming or help abuse. We propose that some examples of this behavior are not abusive and that bottom-out hints can act as worked examples. We create a model for distinguishing good student use of bottom-out hints from bad student use of bottom-out hints by means of logged response (...)
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  47.  25
    Teaching the Normative Theory of Causal Reasoning.Richard Scheines, Matt Easterday & David Danks - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 119--38.
    There is now substantial agreement about the representational component of a normative theory of causal reasoning: Causal Bayes Nets. There is less agreement about a normative theory of causal discovery from data, either computationally or cognitively, and almost no work investigating how teaching the Causal Bayes Nets representational apparatus might help individuals faced with a causal learning task. Psychologists working to describe how naïve participants represent and learn causal structure from data have focused primarily on learning from single trials under (...)
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  48.  28
    On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables.Clark Glymour & Richard Scheines - unknown
    We show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment, log2(N ) + 1 experiments are sufficient and in the worst case necessary to determine the causal relations among N ≥ 2 variables when no latent variables, no sample selection bias and no feedback cycles are present. For all K, 0 < K <.
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  49.  34
    The Tetrad Project: Constraint Based Aids to Causal Model Specification.Richard Scheines - unknown
    The statistical community has brought logical rigor and mathematical precision to the problem of using data to make inferences about a model’s parameter values. The TETRAD project, and related work in computer science and statistics, aims to apply those standards to the problem of using data and background knowledge to make inferences about a model’s specification. We begin by drawing the analogy between parameter estimation and model specification search. We then describe how the specification of a structural equation model entails (...)
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  50.  28
    Causation, Statistics, and the Law.Richard Scheines - unknown
    More and more, judges and juries are being asked to handle torts and other cases in which establishing liability involves understanding large bodies of complex scientific evidence. When establishing causation is involved, the evidence can be diverse, can involve complicated statistical models, and can seem impenetrable to non-experts. Since the decision in Daubert v. Merril Dow Pharms., Inc.1 in 1993, judges cannot simply admit expert testimony and other technical evidence and let jurors decide the verdict. Judges now must rule on (...)
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