10 found
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  1. 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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  2.  17
    Strong-Completeness and Faithfulness in Belief Networks.Christopher Meek - unknown
    Chris Meek. Strong-Completeness and Faithfulness in Belief Networks.
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  3.  18
    Causal Inference in the Presence of Latent Variables and Selection Bias.Peter Spirtes, Christopher Meek & Thomas Richardson - unknown
    Whenever the use of non-experimental data for discovering causal relations or predicting the outcomes of experiments or interventions is contemplated, two difficulties are routinely faced. One is the problem of latent variables, or confounders: factors influencing two or more measured variables may not themselves have been measured or recorded. The other is the problem of sample selection bias: values of the variables or features under study may themselves influence whether a unit is included in the data sample.
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  4.  23
    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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  5.  49
    Using D-Separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors.Peter Spirtes, Thomas Richardson, Christopher Meek, Richard Scheines & Clark Glymour - unknown
    It has been shown in Spirtes(1995) that X and Y are d-separated given Z in a directed graph associated with a recursive or non-recursive linear model without correlated errors if and only if the model entails that ρXY.Z = 0. This result cannot be directly applied to a linear model with correlated errors, however, because the standard graphical representation of a linear model with correlated errors is not a directed graph. The main result of this paper is to show how (...)
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  6. On the Incompatibility of Faithfulness and Monotone DAG Faithfulness.David Maxwell Chickering & Christopher Meek - 2006 - Artificial Intelligence 170 (8-9):653-666.
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  7.  48
    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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  8.  30
    Complete Orientation Rules for Patterns.Christopher Meek - unknown
    Christopher Meek. Complete Orientation Rules for Patterns.
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  9.  22
    Related Graphical Frameworks: Undircted, Directed Acyclic and Chain Graph Models.Christopher Meek - unknown
    Christopher Meek. Related Graphical Frameworks: Undircted, Directed Acyclic and Chain Graph Models.
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    An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality.Gregory F. Cooper, Constantin F. Aliferis, Richard Ambrosino, John Aronis, Bruce G. Buchanon, Richard Caruana, Michael J. Fine, Clark Glymour, Geoffrey Gordon, Barbara H. Hanusa, Janine E. Janosky, Christopher Meek, Tom Mitchell, Thomas Richardson & Peter Spirtes - unknown
    This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model’s potential to assist (...)
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