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

999 found
Sort by:
  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: 3810.0
    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 (...)
    Direct download (9 more)  
     
    My bibliography  
     
    Export citation  
  2. Jiji Zhang & Peter Spirtes, A Characterization of Markov Equivalence Classes for Ancestral Graphical Models.score: 504.0
    JiJi Zhang and Peter Spirtes. A Characterization of Markov Equivalence Classes for Ancestral Graphical Models.
    No categories
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  3. James M. Robins, Richard Scheines, Peter Spirtes & Larry Wasserman, The Limits of Causal Knowledge.score: 504.0
    James M. Robins, Richard Scheines, Peter Spirtes, and Larry Wasserman. The Limits of Causal Knowledge.
    No categories
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  4. Jiji Zhang & Peter Spirtes, A Transformational Characterization of Markov Equivalence Between DAGs with Latent Variables.score: 504.0
    JiJi Zhang and Peter Spirtes. A Transformational Characterization of Markov Equivalence between DAGs with Latent Variables.
    No categories
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  5. Clark Glymour, Richard Scheines, Peter Spirtes & Christopher Meek, Regression and Causation.score: 504.0
    Clark Glymour, Richard Scheines, Peter Spirtes, and Christopher Meek. Regression and Causation.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  6. Peter Spirtes, Discovering Causal Relations Among Latent Variables in Directed Acyclical Graphical Models.score: 504.0
    Peter Spirtes. Discovering Causal Relations Among Latent Variables in Directed Acyclical Graphical Models.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  7. Peter Spirtes & Richard Scheines, Causal Inference and Ambiguous Manipulations.score: 504.0
    Peter Spirtes and Richard Scheines. Causal Inference and Ambiguous Manipulations.
    No categories
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  8. Peter Spirtes, Conditional Independence in Directed Cyclic Graphical Models for Feedback.score: 504.0
    Peter Spirtes. Conditional Independence in Directed Cyclic Graphical Models for Feedback.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  9. Thomas Richardson & Peter Spirtes, Parameterizing and Scoring Mixed Ancestral Graphs.score: 504.0
    Thomas Richardson and Peter Spirtes. Parameterizing and Scoring Mixed Ancestral Graphs.
    No categories
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  10. Peter Spirtes, Conditional Independence in Directed Cyclical Graphical Models Representing Feedback or Mixtures.score: 504.0
    Peter Spirtes. Conditional Independence in Directed Cyclical Graphical Models Representing Feedback or Mixtures.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  11. Clark Glymour, Richard Scheines, Peter Spirtes & Kevin T. Kelly, Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.score: 504.0
    Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  12. Thomas Richardson & Peter Spirtes, Scoring Ancestral Graph Models.score: 504.0
    Thomas Richardson and Peter Spirtes. Scoring Ancestral Graph Models.
    No categories
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  13. Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, S. Fineberg & E. Slate, Prediction and Experimental Design with Graphical Causal Models.score: 504.0
    Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, S. Fineberg, E. Slate. Prediction and Experimental Design with Graphical Causal Models.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  14. Peter Spirtes, Clark Glymour & Rcihard Scheines, Causality From Probability.score: 504.0
    Peter Spirtes, Clark Glymour and Richard Scheines. Causality From Probability.
    No categories
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  15. Peter Spirtes & Clark N. Glymour, Causal Structure Among Measured Variables Preserved with Unmeasured Variables.score: 504.0
    Peter Spirtes and Clark Glymour. Casual Structure Among Measured Variables Preserved with Unmeasured Variables.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  16. Peter Spirtes & Thomas Verma, Equivalence of Causal Models with Latent Variables.score: 504.0
    Peter Spirtes and Thomas Verma. Equivalence of Causal Models with Latent Variables.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  17. Peter Spirtes, Richard Scheines & Clark Glymour, Simulated Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD, EQS and LISREL Programs.score: 504.0
    Peter Spirtes, Richard Scheines and Clark Glymour. Simulated Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD, EQS and LISREL Programs.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  18. Peter Spirtes, Building Causal Graphs From Statistical Data in the Presence of Latent Variables.score: 504.0
    Peter Spirtes. Building Causal Graphs from Statistical Data in the Presence of Latent Variables.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  19. Peter Spirtes, Calculating TETRAD Constraints Implied by Directed Acyclic Graphs.score: 504.0
    Peter Spirtes. Calculating TETRAD Constraints Implied by Directed Acyclic Graphs.
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  20. 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.score: 384.0
    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 (...)
    Translate to English
    | Direct download  
     
    My bibliography  
     
    Export citation  
  21. Peter Spirtes, Uniform Consistency in Causal Inference.score: 300.0
    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 (...)
    No categories
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  22. Peter Spirtes, Thomas Richardson, Christopher Meek, Richard Scheines & Clark Glymour, Using D-Separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors.score: 300.0
    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 (...)
    No categories
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  23. Richard Scheines & Peter Spirtes, Uniform Consistency in Causal Inference.score: 300.0
    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. (...)
    No categories
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  24. Peter Spirtes (2011). Intervention, Determinism, and the Causal Minimality Condition. Synthese 182 (3):335-347.score: 240.0
    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 (...)
    Direct download (8 more)  
     
    My bibliography  
     
    Export citation  
  25. Peter Spirtes (2005). Graphical Models, Causal Inference, and Econometric Models. Journal of Economic Methodology 12 (1):3-34.score: 240.0
    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 (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  26. Peter Spirtes, An Anytime Algorithm for Causal Inference.score: 240.0
    The Fast Casual Inference (FCI) algorithm searches for features common to observationally equivalent sets of causal directed acyclic graphs. It is correct in the large sample limit with probability one even if there is a possibility of hidden variables and selection bias. In the worst case, the number of conditional independence tests performed by the algorithm grows exponentially with the number of variables in the data set. This affects both the speed of the algorithm and the accuracy of the algorithm (...)
    No categories
    Direct download  
     
    My bibliography  
     
    Export citation  
  27. Jiji Zhang & Peter Spirtes (2008). Detection of Unfaithfulness and Robust Causal Inference. Minds and Machines 18 (2):239-271.score: 240.0
    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 (...)
    Direct download (9 more)  
     
    My bibliography  
     
    Export citation  
  28. Peter Spirtes, Clark Glymour & Richard Scheines (1991). From Probability to Causality. Philosophical Studies 64 (1):1 - 36.score: 240.0
    No categories
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  29. 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 (...)
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  30. Peter Spirtes, A Polynomial Time Algorithm for Determining Dag Equivalence in the Presence of Latent Variables and Selection Bias.score: 240.0
    if and only if for every W in V, W is independent of the set of all its non-descendants conditional on the set of its parents. One natural question that arises with respect to DAGs is when two DAGs are “statistically equivalent”. One interesting sense of “statistical equivalence” is “d-separation equivalence” (explained in more detail below.) In the case of DAGs, d-separation equivalence is also corresponds to a variety of other natural senses of statistical equivalence (such as representing the same (...)
    No categories
    Direct download  
     
    My bibliography  
     
    Export citation  
  31. Peter Spirtes, The Limits of Causal Inference From Observational Data.score: 240.0
    The following quotation from Rosenbaum (1995) expresses a commonly held view about the problem of potential confounders, and how they can be dealt with. (We will take a “confounder” of treatment and response to be a variable that is a cause of both treatment and response.).
    Direct download  
     
    My bibliography  
     
    Export citation  
  32. 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 (...)
    No categories
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  33. Peter Spirtes, A Tutorial On Causal Inference.score: 240.0
    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.
    No categories
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  34. Peter Spirtes, Variable Definition and Causal Inference.score: 240.0
    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.
    No categories
    Direct download  
     
    My bibliography  
     
    Export citation  
  35. 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 (...)
    No categories
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  36. 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 (...)
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  37. Peter Spirtes & Clark Glymour (1982). Space-Time and Synonymy. Philosophy of Science 49 (3):463-477.score: 240.0
    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 (...)
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  38. Peter Spirtes, An Algorithm for Fast Recovery of Sparse Causal Graphs.score: 240.0
    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..
    No categories
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  39. Peter Spirtes & Richard Scheines (2004). Causal Inference of Ambiguous Manipulations. Philosophy of Science 71 (5):833-845.score: 240.0
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  40. Peter Spirtes, Heuristic Greedy Search Algorithms for Latent Variable Models.score: 240.0
    A Bayesian network consists of two distinct parts: a directed acyclic graph (DAG or belief-network structure) and a set of parameters for the DAG. The DAG in a Bayesian network can be used to represent both causal hypotheses and sets of probability distributions. Under the causal interpretation, a DAG represents the causal relations in a given population with a set of vertices V when there is an edge from A to B if and only if A is a direct cause (...)
    Direct download  
     
    My bibliography  
     
    Export citation  
  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.score: 240.0
    Direct download (9 more)  
     
    My bibliography  
     
    Export citation  
  42. 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.score: 240.0
    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 (...)
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  43. Thomas Richardson & Peter Spirtes, Ancestral Graph Markov Models.score: 240.0
    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.
    No categories
    Translate to English
    | Direct download  
     
    My bibliography  
     
    Export citation  
  44. Richard Scheines, Peter Spirtes & Clark Glymour, Building Latent Variable Models'.score: 240.0
    Researchers routinely face the problem of inferring causal relationships from large amounts of data, sometimes involving hundreds of variables. Often, it is the causal relationships between "latent" (unmeasured) variables that are of primary interest. The problem is how causal relationships between unmeasured variables can be inferred from measured data. For example, naval manpower researchers have been asked to infer the causal relations among psychological traits such as job satisfaction and job challenge from a data base in which neither trait is (...)
    No categories
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  45. Peter Spirtes, Christopher Meek & Thomas Richardson, Causal Inference in the Presence of Latent Variables and Selection Bias.score: 240.0
    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.
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  46. 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 (...)
    No categories
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  47. Peter Spirtes & Clark Glymour, With Unmeasured Variables.score: 240.0
    In recent papers we have described a framework for inferring causal structure from relations of statistical independence among a set of measured variables. Using Pearl's notion of the perfect representation of a set of independence relations by a directed acyclic graph we proved..
    No categories
    Translate to English
    | Direct download  
     
    My bibliography  
     
    Export citation  
  48. Clark Glymour, Peter Spirtes & Richard Scheines (1990). Independence Relations Produced by Parameter Values in Causal Models. Philosophical Topics 18 (2):55-70.score: 240.0
    No categories
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  49. Kevin Kelly & Peter Spirtes, The Expected Complexity of Problem Solving.score: 240.0
    Worst case complexity analyses of algorithms are sometimes held to be less informative about the real difficulty of computation than are expected complexity analyses. We show that the two most common representations of problem solving in cognitive science each admit aigorithms that have constant expected complexity, and for one of these representations we obtain constant expected complexity bounds under a variety of probability measures.
    No categories
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
1 — 50 / 999