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Peter Spirtes [62]Peter Laurence Spirtes [1]
  1. Causation, Prediction, and Search.Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
  2. Causation, Prediction, and Search.Peter Spirtes, Clark Glymour, Scheines N. & Richard - 2000 - Mit Press: Cambridge.
  3.  90
    Detection of Unfaithfulness and Robust Causal Inference.Jiji Zhang & Peter Spirtes - 2008 - 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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  4.  37
    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.  69
    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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  6.  33
    The Three Faces of Faithfulness.Jiji Zhang & Peter Spirtes - 2016 - 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 we (...)
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  7.  95
    Intervention, Determinism, and the Causal Minimality Condition.Peter Spirtes - 2011 - 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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  8.  43
    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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  9.  30
    An Algorithm for Fast Recovery of Sparse Causal Graphs.Peter Spirtes - unknown
    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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  10.  17
    A Uniformly Consistent Estimator of Causal Effects Under the K-Triangle-Faithfulness Assumption.Peter Spirtes & Jiji Zhang - unknown
    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 B¨uhlmann (...)
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  11. Variable Definition and Causal Inference.Peter Spirtes - manuscript
    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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  12. 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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  13.  8
    Learning Measurement Models for Unobserved Variables.Ricardo Silva, Richard Scheines, Clark Glymour & Peter Spirtes - unknown
  14. An Anytime Algorithm for Causal Inference.Peter Spirtes - unknown
    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 (...)
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  15.  35
    From Probability to Causality.Peter Spirtes, Clark Glymour & Richard Scheines - 1991 - Philosophical Studies 64 (1):1 - 36.
  16.  34
    The Palmer House Hilton Hotel, Chicago, Illinois February 18–20, 2010.Kenneth Easwaran, Philip Ehrlich, David Ross, Christopher Hitchcock, Peter Spirtes, Roy T. Cook, Jean-Pierre Marquis, Stewart Shapiro & Royt Cook - 2010 - Bulletin of Symbolic Logic 16 (3).
  17. Ancestral Graph Markov Models.Thomas Richardson & Peter Spirtes - unknown
    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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  18.  67
    Learning the Structure of Linear Latent Variable Models.Peter Spirtes - unknown
    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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  19.  11
    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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  20.  21
    A Fast Algorithm for Discovering Sparse Causal Graphs.Peter Spirtes & Clark Glymour - unknown
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  21.  26
    Space-Time and Synonymy.Peter Spirtes & Clark Glymour - 1982 - 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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  22.  50
    Graphical Models, Causal Inference, and Econometric Models.Peter Spirtes - 2005 - 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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  23.  39
    Uniform Consistency in Causal Inference.Richard Scheines & Peter Spirtes - unknown
    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. These results (...)
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  24.  20
    Causality From Probability.Peter Spirtes, Clark Glymour & Rcihard Scheines - unknown
    Peter Spirtes, Clark Glymour and Richard Scheines. Causality From Probability.
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  25.  31
    Adjacency-Faithfulness and Conservative Causal Inference.Joseph Ramsey, Jiji Zhang & Peter Spirtes - 2006 - In R. Dechter & T. Richardson (eds.), Proceedings of the Twenty-Second Conference Conference on Uncertainty in Artificial Intelligence (2006). Arlington, Virginia: AUAI Press. pp. 401-408.
    Most causal discovery algorithms in the literature exploit an assumption usually referred to as the Causal Faithfulness or Stability Condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call “Adjacency-Faithfulness” and “Orientation- Faithfulness.” We point out that assuming Adjacency-Faithfulness is true, it is possible to test the validity of Orientation- Faithfulness. Motivated by this observation, we explore the consequence of making only the Adjacency-Faithfulness assumption. We show that the familiar PC algorithm has (...)
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  26.  29
    Causality From Probability.Peter Spirtes, Clark Glymour & Richard Scheines - unknown
    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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  27. A Transformational Characterization of Markov Equivalence for Directed Maximal Ancestral Graphs.Jiji Zhang & Peter Spirtes - unknown
    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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  28.  29
    A Tutorial On Causal Inference.Peter Spirtes - unknown
    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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  29.  27
    A Characterization of Markov Equivalence Classes for Ancestral Graphical Models.Jiji Zhang & Peter Spirtes - unknown
    JiJi Zhang and Peter Spirtes. A Characterization of Markov Equivalence Classes for Ancestral Graphical Models.
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  30.  24
    The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search.David Danks, Clark Glymour & Peter Spirtes - 2003 - 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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  31. With Unmeasured Variables.Peter Spirtes & Clark Glymour - unknown
    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..
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  32.  21
    A Transformational Characterization of Markov Equivalence Between DAGs with Latent Variables.Jiji Zhang & Peter Spirtes - unknown
    JiJi Zhang and Peter Spirtes. A Transformational Characterization of Markov Equivalence between DAGs with Latent Variables.
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  33.  10
    Equivalence of Causal Models with Latent Variables.Peter Spirtes & Thomas Verma - unknown
    Peter Spirtes and Thomas Verma. Equivalence of Causal Models with Latent Variables.
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  34.  18
    Automated Search for Causal Relations - Theory and Practice.Peter Spirtes, Clark Glymour & Richard Scheines - unknown
    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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  35.  8
    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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  36.  14
    Building Latent Variable Models'.Richard Scheines, Peter Spirtes & Clark Glymour - unknown
    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 (...)
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  37. A Polynomial Time Algorithm for Determining Dag Equivalence in the Presence of Latent Variables and Selection Bias.Peter Spirtes - unknown
    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 (...)
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  38.  16
    Using Path Diagrams as a Structural Equation Modelling Tool.Peter Spirtes, Thomas Richardson, Chris Meek & Richard Scheines - unknown
    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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  39.  6
    Exploring Causal Structure with the TETRAD Program.Clark Glymour, Richard Scheines & Peter Spirtes - unknown
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  40.  16
    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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  41.  14
    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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  42. The Limits of Causal Inference From Observational Data.Peter Spirtes - unknown
    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.).
     
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  43.  12
    Scoring Ancestral Graph Models.Thomas Richardson & Peter Spirtes - unknown
    Thomas Richardson and Peter Spirtes. Scoring Ancestral Graph Models.
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  44.  12
    Discovering Causal Relations Among Latent Variables in Directed Acyclical Graphical Models.Peter Spirtes - unknown
    Peter Spirtes. Discovering Causal Relations Among Latent Variables in Directed Acyclical Graphical Models.
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  45.  6
    Directed Cyclic Graphs, Conditional Independence, and Non-Recursive Linear Structural Equation Models.Peter Spirtes - unknown
    Recursive linear structural equation models can be represented by directed acyclic graphs. When represented in this way, they satisfy the Markov Condition. Hence it is possible to use the graphical d-separation to determine what conditional independence relations are entailed by a given linear structural equation model. I prove in this paper that it is also possible to use the graphical d-separation applied to a cyclic graph to determine what conditional independence relations are entailed to hold by a given non-recursive linear (...)
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  46.  11
    The Expected Complexity of Problem Solving.Kevin Kelly & Peter Spirtes - unknown
    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.
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  47.  11
    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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  48.  10
    Conditional Independence in Directed Cyclic Graphical Models for Feedback.Peter Spirtes - unknown
    Peter Spirtes. Conditional Independence in Directed Cyclic Graphical Models for Feedback.
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  49.  9
    Conditional Independence in Directed Cyclical Graphical Models Representing Feedback or Mixtures.Peter Spirtes - unknown
    Peter Spirtes. Conditional Independence in Directed Cyclical Graphical Models Representing Feedback or Mixtures.
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  50.  4
    Latent Variables, Causal Models, and Overidentifying Constraints.Clark Glymour & Peter Spirtes - unknown
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