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Peter Spirtes [62]P. Spirtes [4]Pater Spirtes [1]Peter Laurence Spirtes [1]
  1.  5
    Review: The Grand Leap; Reviewed Work: Causation, Prediction, and Search. [REVIEW]Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
  2. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.J. Pearl, F. Bacchus, P. Spirtes, C. Glymour & R. Scheines - 1988 - Synthese 104 (1):161-176.
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  3.  98
    Causation, Prediction, and Search.Peter Spirtes, Clark Glymour, Scheines N. & Richard - 2000 - Mit Press: Cambridge.
  4.  81
    Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling.Clark Glymour, Richard Scheines, Peter Spirtes & Kevin Kelly - 1987 - Academic Press.
    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. 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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  6.  94
    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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  7.  73
    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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  8. 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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  9.  52
    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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  10.  50
    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.  58
    Causal Inference.C. Glymour, P. Spirtes & R. Scheines - 1991 - Erkenntnis 35 (1-3):151 - 189.
    We have examined only a few of the basic questions about causal inference that result from Reichenbach's two principles. We have not considered what happens when the probability distribution is a mixture of distributions from different causal structures, or how unmeasured common causes can be detected, or what inferences can reliably be drawn about causal relations among unmeasured variables, or the exact advantages that experimental control offers. A good deal is known about these questions, and there is a good deal (...)
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  12. 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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  13.  58
    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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  14.  87
    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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  15.  72
    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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  16.  12
    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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  17.  41
    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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  18.  55
    From Probability to Causality.Peter Spirtes, Clark Glymour & Richard Scheines - 1991 - Philosophical Studies 64 (1):1 - 36.
  19.  25
    Learning Measurement Models for Unobserved Variables.Ricardo Silva, Richard Scheines, Clark Glymour & Peter Spirtes - unknown
  20. 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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  21. 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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  22. 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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  23.  16
    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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  24.  61
    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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  25.  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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  26.  34
    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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  27.  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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  28. 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.
  29.  71
    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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  30.  78
    A Fast Algorithm for Discovering Sparse Causal Graphs.Peter Spirtes & Clark Glymour - unknown
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  31.  45
    Discussion. Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search.P. Spirtes - 1997 - British Journal for the Philosophy of Science 48 (4):555-568.
  32.  40
    Causality From Probability.Peter Spirtes, Clark Glymour & Rcihard Scheines - unknown
  33.  21
    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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  34.  7
    A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables.Jiji Zhang & Peter Spirtes - unknown
    Different directed acyclic graphs may be Markov equivalent in the sense that they entail the same conditional independence relations among the observed variables. Chickering provided a transformational characterization of Markov equivalence for DAGs, which is useful in deriving properties shared by Markov equivalent DAGs, and, with certain generalization, is needed to prove the asymptotic correctness of a search procedure over Markov equivalence classes, known as the GES algorithm. For DAG models with latent variables, maximal ancestral graphs provide a neat representation (...)
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  35.  35
    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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  36.  10
    Latent Variables, Causal Models, and Overidentifying Constraints.Clark Glymour & Peter Spirtes - unknown
  37.  8
    Causal Structure Among Measured Variables Preserved with Unmeasured Variables.Peter Spirtes & Clark N. Glymour - unknown
    Peter Spirtes and Clark Glymour. Casual Structure Among Measured Variables Preserved with Unmeasured Variables.
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  38.  8
    Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables.Ayesha Ali, Thomas Richardson, Peter Spirtes & Jiji Zhang - unknown
    It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one representation. In this paper, we address what is less well known: how do the relationships common to every causal explanation among the observed variables of some DAG process change in the presence of latent variables? Ancestral graphs provide a class of graphs that can encode conditional (...)
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  39.  29
    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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  40.  66
    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).
  41.  9
    Discussion of Causal Diagrams for Empirical Research by J. Pearl.Stephen E. Fienberg, Clark Glymour & Peter Spirtes - unknown
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  42.  14
    Exploring Causal Structure with the TETRAD Program.Clark Glymour, Richard Scheines & Peter Spirtes - unknown
  43.  47
    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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  44. Causality Workbench.Isabelle Guyon, C. Aliferis, G. Cooper, A. Elisseeff J.-P. Pellet, P. Spirtes & A. Statnikov - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
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  45.  15
    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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  46.  20
    Parameterizing and Scoring Mixed Ancestral Graphs.Thomas Richardson & Peter Spirtes - unknown
    Thomas Richardson and Peter Spirtes. Parameterizing and Scoring Mixed Ancestral Graphs.
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  47.  23
    Scoring Ancestral Graph Models.Thomas Richardson & Peter Spirtes - unknown
    Thomas Richardson and Peter Spirtes. Scoring Ancestral Graph Models.
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  48.  20
    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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  49.  28
    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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  50.  18
    Analysis of Microarray Data for Treated Fat Cells.Nicoleta Serban, Larry Wasserman, David Peters, Peter Spirtes, Robert O'Doherty, Daniel Handley, Richard Scheines & Clark Glymour - unknown
    DNA microarrays are perfectly suited for comparing gene expression in different populations of cells. An important application of microarray techniques is identifying genes which are activated by a particular drug of interest. This process will allow biologists to identify therapies targeted to particular diseases, and, eventually, to gain more knowledge about the biological processes in organisms. Such an application is described in this paper. It is focused on diabetes and obesity, which is a genetically heterogeneous disease, meaning that multiple defective (...)
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