Search results for 'Richard Scheines Joseph Ramsey' (try it on Scholar)

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  1. 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.score: 19200.0
    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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  2. Richard Scheines & Joe Ramsey, Simulating Genetic Regulartory Networks.score: 16650.0
    Richard Scheines and Joe Ramsey. Simulating Genetic Regulartory Networks.
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  3. Andrew Arnold, Richard Scheines, Joseph E. Back & Bill Jerome, Time and Attention: Students, Sessions, and Tasks.score: 855.0
    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. Aurora P. Jackson & Richard Scheines, Single Mother's Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study.score: 510.0
    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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  5. James M. Robins, Richard Scheines, Peter Spirtes & Larry Wasserman, The Limits of Causal Knowledge.score: 510.0
    James M. Robins, Richard Scheines, Peter Spirtes, and Larry Wasserman. The Limits of Causal Knowledge.
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  6. Clark Glymour, Richard Scheines, Peter Spirtes & Christopher Meek, Regression and Causation.score: 510.0
    Clark Glymour, Richard Scheines, Peter Spirtes, and Christopher Meek. Regression and Causation.
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  7. Richard Scheines, Gaea Leinhardt, Joel Smith & Kwangsu Cho, Teaching and Learning with Online Courses.score: 510.0
    Richard Scheines, Gaea Leinhardt, Joel Smith, and Kwangsu Cho. Teaching and Learning with Online Courses.
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  8. Peter Spirtes & Richard Scheines, Causal Inference and Ambiguous Manipulations.score: 510.0
    Peter Spirtes and Richard Scheines. Causal Inference and Ambiguous Manipulations.
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  9. Clark Glymour, Richard Scheines, Peter Spirtes & Kevin T. Kelly, Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.score: 510.0
    Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
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  10. Shane Harwood & Richard Scheines, Learning Linear Causal Structure Equation Models with Genetic Algorithms.score: 510.0
    Shane Harwood and Richard Scheines. Learning Linear Causal Structure Equation Models with Genetic Algorithms.
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  11. Richard Scheines & Wilfried Sieg, Computer Environments for Proof Construction.score: 510.0
    Richard Scheines and Wilfred Sieg. Computer Environments for Proof Construction.
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  12. Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, S. Fineberg & E. Slate, Prediction and Experimental Design with Graphical Causal Models.score: 510.0
    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. Shane Harwood & Richard Scheines, Genetic Algorithm Search Over Causal Models.score: 510.0
    Shane Harwood and Richard Scheines. Genetic Algorithm Search Over Causal Models.
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  14. Peter Spirtes, Richard Scheines & Clark Glymour, Simulated Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD, EQS and LISREL Programs.score: 510.0
    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. William Ramsey (2010). Review of Richard Menary (Ed.), The Extended Mind. [REVIEW] Notre Dame Philosophical Reviews 2010 (12).score: 360.0
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  16. Jean Richard (1978). COPPENS, Joseph, éd., La notion biblique de Dieu. Le Dieu de la Bible et le Dieu des philosophes. Laval Théologique et Philosophique 34 (3):324-332.score: 360.0
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  17. William Ramsey & Stephen Stich (1995). And Joseph Garon. In Cynthia Macdonald & Graham Macdonald (eds.), Connectionism: Debates on Psychological Explanation. Blackwell. 311.score: 360.0
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  18. Paul Ramsey (1976). A REPLY TO RICHARD McCORMICK: The Enforcement of Morals: Nontherapeutic Research on Children. Hastings Center Report 6 (4):21-30.score: 360.0
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  19. Peter Spirtes, Clark Glymour & Rcihard Scheines, Causality From Probability.score: 330.0
    Peter Spirtes, Clark Glymour and Richard Scheines. Causality From Probability.
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  20. Frederick Eberhardt & Richard Scheines (2007). Interventions and Causal Inference. Philosophy of Science 74 (5):981-995.score: 300.0
    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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  21. Richard Scheines, Reply to Freedman.score: 300.0
    In Causation, Prediction, and Search (Spirtes, Glymour, and Scheines 1993), we undertook a three part project. (Henceforth we will refer to Causation, Prediction, and Search as CPS.) First, we characterized when causal models are indistinguishable by population conditional independence relations under several different assumptions relating causality to probability. Second, we proposed a number of algorithms that take sample data and optional background knowledge as input, and output a class of causal models compatible with the data and the background knowledge; (...)
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  22. Joseph Ramsey, Bootstrapping the PC and CPC Algorithms to Improve Search Accuracy.score: 300.0
    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. Jonathan Livengood, Justin Sytsma, Adam Feltz, Richard Scheines & Edouard Machery (2010). Philosophical Temperament. Philosophical Psychology 23 (3):313-330.score: 240.0
    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. This claim is supported by a study of more than (...)
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  24. William Ramsey, Stephen Stich & Joseph Garon (1990). Connectionism, Eliminativism and the Future of Folk Psychology. Philosophical Perspectives 4:499-533.score: 240.0
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  25. Richard Scheines, An Introduction to Causal Inference.score: 240.0
    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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  26. Gregory Wheeler & Richard Scheines (2013). Coherence and Confirmation Through Causation. Mind 122 (485):135-170.score: 240.0
    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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  27. Clark Glymour & Richard Scheines (1986). Causal Modeling with the TETRAD Program. Synthese 68 (1):37 - 63.score: 240.0
    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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  28. Gregory Wheeler & Richard Scheines (2011). Causation, Association and Confirmation. In Stephan Hartmann, Marcel Weber, Wenceslao Gonzalez, Dennis Dieks & Thomas Uebe (eds.), Explanation, Prediction, and Confirmation: New Trends and Old Ones Reconsidered. Springer. 37--51.score: 240.0
    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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  29. Richard Scheines, Causation.score: 240.0
    Practically, causation matters. Juries must decide, for example, whether a pregnant mother’s refusal to give birth by caesarean section was the cause of one of her twins death. Policy makers must decide whether violence on TV causes violence in life. Neither question can be coherently debated without some theory of causation. Fortunately (or not, depending on where one sits), a virtual plethora of theories of causation have been championed in the third of a century between 1970 and 2004.
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  30. Richard Scheines (2005). The Similarity of Causal Inference in Experimental and Non-Experimental Studies. Philosophy of Science 72 (5):927-940.score: 240.0
    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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  31. Richard Scheines, Causation, Truth, and the Law.score: 240.0
    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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  32. Peter Spirtes, Clark Glymour & Richard Scheines (1991). From Probability to Causality. Philosophical Studies 64 (1):1 - 36.score: 240.0
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  33. Peter Spirtes, Thomas Richardson, Chris Meek & Richard Scheines, Using Path Diagrams as a Structural Equation Modelling Tool.score: 240.0
    Linear structural equation models (SEMs) are widely used in sociology, econometrics, biology, and other sciences. A SEM (without free parameters) has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the “error” or “disturbance” terms), and an associated path diagram corresponding to the functional composition of variables specified by the structural equations and the correlations among the error terms. It is often thought that the path diagram is (...)
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  34. H. W. B. Joseph, F. P. Ramsey & R. B. Braithwaite (1926). Symposium: Universals and the "Method of Analysis". Aristotelian Society Supplementary Volume 6:1 - 38.score: 240.0
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  35. Richard Scheines, The Tetrad Project: Constraint Based Aids to Causal Model Specification.score: 240.0
    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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  36. Peter Spirtes, Clark Glymour & Richard Scheines, Automated Search for Causal Relations - Theory and Practice.score: 240.0
    nature of modern data collection and storage techniques, and the increases in the speed and storage capacities of computers. Statistics books from 30 years ago often presented examples with fewer than 10 variables, in domains where some background knowledge was plausible. In contrast, in new domains, such as climate research where satellite data now provide daily quantities of data unthinkable a few decades ago, fMRI brain imaging, and microarray measurements of gene expression, the number of variables can range into the (...)
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  37. Peter Spirtes, Clark Glymour & Richard Scheines, Causality From Probability.score: 240.0
    Data analysis that merely fits an empirical covariance matrix or that finds the best least squares linear estimator of a variable is not of itself a reliable guide to judgements about policy, which inevitably involve causal conclusions. The policy implications of empirical data can be completely reversed by alternative hypotheses about the causal relations of variables, and the estimates of a particular causal influence can be radically altered by changes in the assumptions made about other dependencies.2 For these reasons, one (...)
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  38. Clark Glymour & Richard Scheines, On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables.score: 240.0
    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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  39. Richard Scheines, Clark Glymour & Peter Spirtes, Learning the Structure of Linear Latent Variable Models.score: 240.0
    We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded (...)
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  40. Richard Scheines (2002). Computation and Causation. In James Moor & Terrell Ward Bynum (eds.), Cyberphilosophy: The Intersection of Philosophy and Computing. Blackwell Pub.. 158-180.score: 240.0
    In 1982, when computers were just becoming widely available, I was a graduate student beginning my work with Clark Glymour on a PhD thesis entitled: “Causality in the Social Sciences.” Dazed and confused by the vast philosophical literature on causation, I found relative solace in the clarity of Structural Equation Models (SEMs), a form of statistical model used commonly by practicing sociologists, political scientists, etc., to model causal hypotheses with which associations among measured variables might be explained. The statistical literature (...)
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  41. Peter Spirtes & Richard Scheines (2004). Causal Inference of Ambiguous Manipulations. Philosophy of Science 71 (5):833-845.score: 240.0
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  42. Richard Scheines, Estimating Latent Causal Influences: Tetrad III Variable Selection and Bayesian Parameter Estimation.score: 240.0
    The statistical evidence for the detrimental effect of exposure to low levels of lead on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics help make the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD III for finding causes, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead Exposure, a (...)
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  43. Keith Lehrer & Joseph Richard (1975). Remembering Without Knowing. Grazer Philosophische Studien 1:121-126.score: 240.0
    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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  44. Richard Scheines, Bayesian Estimation and Testing of Structural Equation Models.score: 240.0
    The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those (...)
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  45. Richard Scheines, Causation, Statistics, and the Law.score: 240.0
    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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  46. Richard Scheines, Expert Statistical Testimony and Epidemiological Evidence: The Toxic Effects of Lead Exposure on Children.score: 240.0
    The past two decades have seen a dramatic growth in the use of statisticians and economists for the presentation of expert testimony in legal proceedings. In this paper, we describe a hypothetical case modeled on real ones and involving statistical testimony regarding the causal effect of lead on lowering the IQs of children who ingest lead paint chips. The data we use come from a well-known pioneering study on the topic and the analyses we describe as the expert testimony are (...)
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  47. Richard Scheines, Piecewise Linear Instrumental Variable Estimation of Causal Influence.score: 240.0
    Dept. of Philosophy Center for Biomedical Center for Biomedical Dept. of Philosophy Carnegie Mellon Univ.
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