Search results for 'Choh Man Teng Peter Spirtes' (try it on Scholar)

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  1.  88
    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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  2.  12
    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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  3.  8
    Peter Spirtes, Clark Glymour & Rcihard Scheines, Causality From Probability.
    Peter Spirtes, Clark Glymour and Richard Scheines. Causality From Probability.
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  4.  15
    Jiji Zhang & Peter Spirtes, A Characterization of Markov Equivalence Classes for Ancestral Graphical Models.
    JiJi Zhang and Peter Spirtes. A Characterization of Markov Equivalence Classes for Ancestral Graphical Models.
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  5.  4
    Peter Spirtes & Thomas Verma, Equivalence of Causal Models with Latent Variables.
    Peter Spirtes and Thomas Verma. Equivalence of Causal Models with Latent Variables.
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  6.  10
    James M. Robins, Richard Scheines, Peter Spirtes & Larry Wasserman, The Limits of Causal Knowledge.
    James M. Robins, Richard Scheines, Peter Spirtes, and Larry Wasserman. The Limits of Causal Knowledge.
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  7.  10
    Peter Spirtes, Discovering Causal Relations Among Latent Variables in Directed Acyclical Graphical Models.
    Peter Spirtes. Discovering Causal Relations Among Latent Variables in Directed Acyclical Graphical Models.
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  8.  8
    Thomas Richardson & Peter Spirtes, Scoring Ancestral Graph Models.
    Thomas Richardson and Peter Spirtes. Scoring Ancestral Graph Models.
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  9.  4
    Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, S. Fineberg & E. Slate, Prediction and Experimental Design with Graphical Causal Models.
    Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, S. Fineberg, E. Slate. Prediction and Experimental Design with Graphical Causal Models.
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  10.  9
    Peter Spirtes, Conditional Independence in Directed Cyclic Graphical Models for Feedback.
    Peter Spirtes. Conditional Independence in Directed Cyclic Graphical Models for Feedback.
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  11.  8
    Peter Spirtes, Conditional Independence in Directed Cyclical Graphical Models Representing Feedback or Mixtures.
    Peter Spirtes. Conditional Independence in Directed Cyclical Graphical Models Representing Feedback or Mixtures.
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  12.  9
    Clark Glymour, Richard Scheines, Peter Spirtes & Christopher Meek, Regression and Causation.
    Clark Glymour, Richard Scheines, Peter Spirtes, and Christopher Meek. Regression and Causation.
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  13.  9
    Jiji Zhang & Peter Spirtes, A Transformational Characterization of Markov Equivalence Between DAGs with Latent Variables.
    JiJi Zhang and Peter Spirtes. A Transformational Characterization of Markov Equivalence between DAGs with Latent Variables.
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  14.  7
    Thomas Richardson & Peter Spirtes, Parameterizing and Scoring Mixed Ancestral Graphs.
    Thomas Richardson and Peter Spirtes. Parameterizing and Scoring Mixed Ancestral Graphs.
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  15.  7
    Peter Spirtes & Richard Scheines, Causal Inference and Ambiguous Manipulations.
    Peter Spirtes and Richard Scheines. Causal Inference and Ambiguous Manipulations.
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  16.  1
    Peter Spirtes & Clark N. Glymour, Causal Structure Among Measured Variables Preserved with Unmeasured Variables.
    Peter Spirtes and Clark Glymour. Casual Structure Among Measured Variables Preserved with Unmeasured Variables.
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  17.  1
    Peter Spirtes, Richard Scheines & Clark Glymour, Simulated Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD, EQS and LISREL Programs.
    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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  18. Peter Spirtes, Building Causal Graphs From Statistical Data in the Presence of Latent Variables.
    Peter Spirtes. Building Causal Graphs from Statistical Data in the Presence of Latent Variables.
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  19. Peter Spirtes, Calculating TETRAD Constraints Implied by Directed Acyclic Graphs.
    Peter Spirtes. Calculating TETRAD Constraints Implied by Directed Acyclic Graphs.
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  20.  33
    David Danks Clark Glymour, Frederick Eberhardt Bruce Glymour, Richard Scheines Joseph Ramsey, Choh Man Teng Peter Spirtes & Jiji Zhang (forthcoming). Actual Causation: A Stone Soup Essay. Synthese.
    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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  21.  15
    Jiji Zhang & Peter Spirtes (2016). The Three Faces of Faithfulness. Synthese 193 (4):1011-1027.
    In the causal inference framework of Spirtes, Glymour, and Scheines, inferences about causal relationships are made from samples from probability distributions and a number of assumptions relating causal relations to probability distributions. The most controversial of these assumptions is the Causal Faithfulness Assumption, which roughly states that if a conditional independence statement is true of a probability distribution generated by a causal structure, it is entailed by the causal structure and not just for particular parameter values. In this paper (...)
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  22.  4
    Peter Spirtes & Jiji Zhang, A Uniformly Consistent Estimator of Causal Effects Under the K-Triangle-Faithfulness Assumption.
    Spirtes, Glymour and Scheines [Causation, Prediction, and Search Springer] described a pointwise consistent estimator of the Markov equivalence class of any causal structure that can be represented by a directed acyclic graph for any parametric family with a uniformly consistent test of conditional independence, under the Causal Markov and Causal Faithfulness assumptions. Robins et al. [Biometrika 90 491–515], however, proved that there are no uniformly consistent estimators of Markov equivalence classes of causal structures under those assumptions. Subsequently, Kalisch and (...)
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  23.  23
    Peter Spirtes, Uniform Consistency in Causal Inference.
    There is a long tradition of representing causal relationships by directed acyclic graphs (Wright, 1934 ). Spirtes ( 1994), Spirtes et al. ( 1993) and Pearl & Verma ( 1991) describe procedures for inferring the presence or absence of causal arrows in the graph even if there might be unobserved confounding variables, and/or an unknown time order, and that under weak conditions, for certain combinations of directed acyclic graphs and probability distributions, are asymptotically, in sample size, consistent. These (...)
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  24.  14
    Richard Scheines & Peter Spirtes, Uniform Consistency in Causal Inference.
    S There is a long tradition of representing causal relationships by directed acyclic graphs (Wright, 1934 ). Spirtes ( 1994), Spirtes et al. ( 1993) and Pearl & Verma ( 1991) describe procedures for inferring the presence or absence of causal arrows in the graph even if there might be unobserved confounding variables, and/or an unknown time order, and that under weak conditions, for certain combinations of directed acyclic graphs and probability distributions, are asymptotically, in sample size, consistent. (...)
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  25.  12
    Peter Spirtes, Thomas Richardson, Christopher Meek, Richard Scheines & Clark Glymour, Using D-Separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors.
    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 (...)
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  26. 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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  27. Peter Spirtes, Clark Glymour, Scheines N. & Richard (2000). Causation, Prediction, and Search. MIT Press: Cambridge.
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  28.  56
    Jiji Zhang & Peter Spirtes (2008). Detection of Unfaithfulness and Robust Causal Inference. Minds and Machines 18 (2):239-271.
    Much of the recent work on the epistemology of causation has centered on two assumptions, known as the Causal Markov Condition and the Causal Faithfulness Condition. Philosophical discussions of the latter condition have exhibited situations in which it is likely to fail. This paper studies the Causal Faithfulness Condition as a conjunction of weaker conditions. We show that some of the weaker conjuncts can be empirically tested, and hence do not have to be assumed a priori. Our results lead to (...)
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  29.  22
    Peter Spirtes & Richard Scheines (2004). Causal Inference of Ambiguous Manipulations. 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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  30.  16
    Peter Spirtes, An Algorithm for Fast Recovery of Sparse Causal Graphs.
    Previous asymptotically correct algorithms for recovering causal structure from sample probabilities have been limited even in sparse graphs to a few variables. We describe an asymptotically correct algorithm whose complexity for fixed graph connectivity increases polynomially in the number of vertices, and may in practice recover sparse graphs with several hundred variables. From..
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  31. 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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  32.  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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  33.  24
    Peter Spirtes, Ancestral Graph Markov Models.
    This paper introduces a class of graphical independence models that is closed under marginalization and conditioning but that contains all DAG independence models. This class of graphs, called maximal ancestral graphs, has two attractive features: there is at most one edge between each pair of vertices; every missing edge corresponds to an independence relation. These features lead to a simple parameterization of the corresponding set of distributions in the Gaussian case.
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  34.  28
    Kenneth Easwaran, Philip Ehrlich, David Ross, Christopher Hitchcock, Peter Spirtes, Roy T. Cook, Jean-Pierre Marquis, Stewart Shapiro & Royt Cook (2010). The Palmer House Hilton Hotel, Chicago, Illinois February 18–20, 2010. Bulletin of Symbolic Logic 16 (3).
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  35.  3
    Jiji Zhang & Peter Spirtes, The Three Faces of Faithfulness.
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  36.  19
    Peter Spirtes, Variable Definition and Causal Inference.
    In the last several decades, a confluence of work in the social sciences, philosophy, statistics, and computer science has developed a theory of causal inference using directed graphs. This theory typically rests either explicitly or implicitly on two major assumptions.
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  37.  20
    Peter Spirtes & Richard Scheines (2004). Causal Inference of Ambiguous Manipulations. Philosophy of Science 71 (5):833-845.
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  38.  65
    Peter Spirtes (2011). Intervention, Determinism, and the Causal Minimality Condition. Synthese 182 (3):335-347.
    We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semantics of causal Bayesian networks. Our argument has two parts. First, we show that the causal minimality condition, rather than an (...)
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  39.  12
    Clark Glymour, Peter Spirtes & Richard Scheines (1990). Independence Relations Produced by Parameter Values in Causal Models. Philosophical Topics 18 (2):55-70.
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  40.  31
    Peter Spirtes, Clark Glymour & Richard Scheines (1991). From Probability to Causality. Philosophical Studies 64 (1):1 - 36.
  41.  44
    Peter Spirtes (2005). Graphical Models, Causal Inference, and Econometric Models. Journal of Economic Methodology 12 (1):3-34.
    A graphical model is a graph that represents a set of conditional independence relations among the vertices (random variables). The graph is often given a causal interpretation as well. I describe how graphical causal models can be used in an algorithm for constructing partial information about causal graphs from observational data that is reliable in the large sample limit, even when some of the variables in the causal graph are unmeasured. I also describe an algorithm for estimating from observational data (...)
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  42.  37
    Richard Scheines, Clark Glymour & Peter Spirtes, Learning the Structure of Linear Latent Variable Models.
    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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  43.  24
    Peter Spirtes & Clark Glymour (1982). Space-Time and Synonymy. Philosophy of Science 49 (3):463-477.
    In "The Epistemology of Geometry" Glymour proposed a necessary structural condition for the synonymy of two space-time theories. David Zaret has recently challenged this proposal, by arguing that Newtonian gravitational theory with a flat, non-dynamic connection (FNGT) is intuitively synonymous with versions of the theory using a curved dynamical connection (CNGT), even though these two theories fail to satisfy Glymour's proposed necessary condition for synonymy. Zaret allowed that if FNGT and CNGT were not equally well (bootstrap) tested by the relevant (...)
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  44.  28
    Thomas Richardson & Peter Spirtes, Ancestral Graph Markov Models.
    This paper introduces a class of graphical independence models that is closed under marginalization and conditioning but that contains all DAG independence models. This class of graphs, called maximal ancestral graphs, has two attractive features: there is at most one edge between each pair of vertices; every missing edge corresponds to an independence relation. These features lead to a simple parameterization of the corresponding set of distributions in the Gaussian case.
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  45.  1
    Ricardo Silva, Richard Scheines, Clark Glymour & Peter Spirtes, Learning Measurement Models for Unobserved Variables.
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  46.  20
    Jiji Zhang & Peter Spirtes, A Transformational Characterization of Markov Equivalence for Directed Maximal Ancestral Graphs.
    The conditional independence relations present in a data set usually admit multiple causal explanations — typically represented by directed graphs — which are Markov equivalent in that they entail the same conditional independence relations among the observed variables. Markov equivalence between directed acyclic graphs (DAGs) has been characterized in various ways, each of which has been found useful for certain purposes. In particular, Chickering’s transformational characterization is useful in deriving properties shared by Markov equivalent DAGs, and, with certain generalization, is (...)
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  47.  21
    David Danks, Clark Glymour & Peter Spirtes (2003). The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search. In W. H. Hsu, R. Joehanes & C. D. Page (eds.), Proceedings of IJCAI-2003 workshop on learning graphical models for computational genomics.
    Various algorithms have been proposed for learning (partial) genetic regulatory networks through systematic measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as for determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (in the (...)
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  48.  24
    Peter Spirtes, A Tutorial On Causal Inference.
    The goal of this tutorial is twofold: to provide a description of some basic causal inference problems, models, algorithms, and assumptions in enough detail to understand recent developments in these areas; and to compare and contrast these with machine learning problems, models, algorithms, and assumptions.
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  49.  21
    Peter Spirtes, Clark Glymour & Richard Scheines, Causality From Probability.
    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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  50.  25
    Peter Spirtes, Learning the Structure of Linear Latent Variable Models.
    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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