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Richard Scheines [63]Richard Paul Scheines [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.  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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  3. Philosophical Temperament.Jonathan Livengood, Justin Sytsma, Adam Feltz, Richard Scheines & Edouard Machery - 2010 - Philosophical Psychology 23 (3):313-330.
    Many philosophers have worried about what philosophy is. Often they have looked for answers by considering what it is that philosophers do. Given the diversity of topics and methods found in philosophy, however, we propose a different approach. In this article we consider the philosophical temperament, asking an alternative question: what are philosophers like? Our answer is that one important aspect of the philosophical temperament is that philosophers are especially reflective: they are less likely than their peers to embrace what (...)
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  4. Coherence and Confirmation Through Causation.Gregory Wheeler & Richard Scheines - 2013 - Mind 122 (485):135-170.
    Coherentism maintains that coherent beliefs are more likely to be true than incoherent beliefs, and that coherent evidence provides more confirmation of a hypothesis when the evidence is made coherent by the explanation provided by that hypothesis. Although probabilistic models of credence ought to be well-suited to justifying such claims, negative results from Bayesian epistemology have suggested otherwise. In this essay we argue that the connection between coherence and confirmation should be understood as a relation mediated by the causal relationships (...)
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  5. Actual Causation: A Stone Soup Essay.Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey & Richard Scheines - 2010 - 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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  6. Interventions and Causal Inference.Frederick Eberhardt & Richard Scheines - 2007 - Philosophy of Science 74 (5):981-995.
    The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard' and ‘soft' interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the (...)
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  7.  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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  8.  51
    The Similarity of Causal Inference in Experimental and Non‐Experimental Studies.Richard Scheines - 2005 - Philosophy of Science 72 (5):927-940.
    For nearly as long as the word “correlation” has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect them to probabilistic relations. One strand of this work studies the conditions under which evidence (...)
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  9.  92
    Causation, Association, and Confirmation.Gregory Wheeler & Richard Scheines - 2010 - In Stephan Hartmann, Marcel Weber, Wenceslao Gonzalez, Dennis Dieks & Thomas Uebe (eds.), Explanation, Prediction, and Confirmation: New Trends and Old Ones Reconsidered. Springer. pp. 37--51.
    Many philosophers of science have argued that a set of evidence that is "coherent" confirms a hypothesis which explains such coherence. In this paper, we examine the relationships between probabilistic models of all three of these concepts: coherence, confirmation, and explanation. For coherence, we consider Shogenji's measure of association (deviation from independence). For confirmation, we consider several measures in the literature, and for explanation, we turn to Causal Bayes Nets and resort to causal structure and its constraint on probability. All (...)
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  10.  18
    The Donor is in the Details.Cynthia E. Cryder, George Loewenstein & Richard Scheines - unknown
    Recent research finds that people respond more generously to individual victims described in detail than to equivalent statistical victims described in general terms. We propose that this “identified victim effect” is one manifestation of a more general phenomenon: a positive influence of tangible information on generosity. In three experiments, we find evidence for an “identified intervention effect”; providing tangible details about a charity’s interventions significantly increases donations to that charity. Although previous work described sympathy as the primary mediator between tangible (...)
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  11.  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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  12.  38
    Bayesian Estimation and Testing of Structural Equation Models.Richard Scheines - unknown
    The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those (...)
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  13.  37
    Searching for Proofs.Wilfried Sieg & Richard Scheines - unknown
    The Carnegie Mellon Proof Tutor project was motivated by pedagogical concerns: we wanted to use a "mechanical" (i.e. computerized) tutor for teaching students..
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  14.  68
    On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables.Clark Glymour & Richard Scheines - unknown
    We show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment, log2(N ) + 1 experiments are sufficient and in the worst case necessary to determine the causal relations among N ≥ 2 variables when no latent variables, no sample selection bias and no feedback cycles are present. For all K, 0 < K <.
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  15.  38
    N − 1 Experiments Suffice to Determine the Causal Relations Among N Variables.Frederick Eberhardt, Clark Glymour & Richard Scheines - unknown
    By combining experimental interventions with search procedures for graphical causal models we show that under familiar assumptions, with perfect data, N - 1 experiments suffice to determine the causal relations among N > 2 variables when each experiment randomizes at most one variable. We show the same bound holds for adaptive learners, but does not hold for N > 4 when each experiment can simultaneously randomize more than one variable. This bound provides a type of ideal for the measure of (...)
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  16.  64
    The Tetrad Project: Constraint Based Aids to Causal Model Specification.Richard Scheines - 1998 - Multivariate Behavioral Research 33 (1):65-117.
    The statistical community has brought logical rigor and mathematical precision to the problem of using data to make inferences about a model’s parameter values. The TETRAD project, and related work in computer science and statistics, aims to apply those standards to the problem of using data and background knowledge to make inferences about a model’s specification. We begin by drawing the analogy between parameter estimation and model specification search. We then describe how the specification of a structural equation model entails (...)
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  17. An Introduction to Causal Inference.Richard Scheines - unknown
    In Causation, Prediction, and Search (CPS hereafter), Peter Spirtes, Clark Glymour and I developed a theory of statistical causal inference. In his presentation at the Notre Dame conference (and in his paper, this volume), Glymour discussed the assumptions on which this theory is built, traced some of the mathematical consequences of the assumptions, and pointed to situations in which the assumptions might fail. Nevertheless, many at Notre Dame found the theory difficult to understand and/or assess. As a result I was (...)
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  18.  55
    From Probability to Causality.Peter Spirtes, Clark Glymour & Richard Scheines - 1991 - Philosophical Studies 64 (1):1 - 36.
  19.  6
    Combining Experiments to Discover Linear Cyclic Models with Latent Variables.Richard Scheines, Frederick Eberhardt & Patrik O. Hoyer - unknown
    We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relations are linear, but is otherwise completely general: It provides consistent estimates when the true causal structure contains feedback loops and latent variables, while the experiments can involve surgical or `soft' interventions on one or multiple variables at a time. The algorithm is `online' in the sense that it combines the results from (...)
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  20.  25
    Learning Measurement Models for Unobserved Variables.Ricardo Silva, Richard Scheines, Clark Glymour & Peter Spirtes - unknown
  21. Combining Experiments to Discover Linear Cyclic Models.Richard Scheines - unknown
    We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relations are linear, but is otherwise completely general: It provides consistent estimates when the true causal structure contains feedback loops and latent variables, while the experiments can involve surgical or ‘soft’ interventions on one or multiple variables at a time. The algorithm is ‘online’ in the sense that it combines the results from (...)
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  22.  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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  23. Reply to Freedman.Richard Scheines - unknown
    In Causation, Prediction, and Search, we undertook a three part project. First, we characterized when causal models are indistinguishable by population conditional independence relations under several different assumptions relating causality to probability. Second, we proposed a number of algorithms that take sample data and optional background knowledge as input, and output a class of causal models compatible with the data and the background knowledge; the algorithms were accompanied by proofs of their correctness given assumptions that were clearly stated in CPS, (...)
     
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  24.  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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  25.  21
    A Response Time Model for Bottom-Out Hints as Worked Examples.Richard Scheines - unknown
    Students can use an educational system’s help in unexpected ways. For example, they may bypass abstract hints in search of a concrete solution. This behavior has traditionally been labeled as a form of gaming or help abuse. We propose that some examples of this behavior are not abusive and that bottom-out hints can act as worked examples. We create a model for distinguishing good student use of bottom-out hints from bad student use of bottom-out hints by means of logged response (...)
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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.  52
    Causation, Truth, and the Law.Richard Scheines - unknown
    Deciding matters of legal liability, in torts and other civil actions, requires deciding causation. The injury suffered by a plaintiff must be caused by an event or condition due to the defendant. The courts distinguish between cause-in-fact and proximate causation, where cause-in-fact is determined by the “but-for” test: the effect would not have happened, “but for” the cause.1 Proximate causation is a set of legal limitations on cause-in-fact.
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  29.  30
    Estimating Latent Causal Influences: Tetrad III Variable Selection and Bayesian Parameter Estimation.Richard Scheines - unknown
    The statistical evidence for the detrimental effect of exposure to low levels of lead on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics help make the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD III for finding causes, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead Exposure, a (...)
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  30. 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.
  31.  23
    Replacing Lecture with Web-Based Course Materials.Richard Scheines, Gaea Leinhardt, Joel Smith & Kwangsu Cho - unknown
    In a series of 5 experiments in 2000 and 2001, several hundred students at two different universities with three different professors and six different teaching assistants took a semester long course on causal and statistical reasoning in either traditional lecture/recitation or online/recitation format. In this paper we compare the pre-post test gains of these students, we identify features of the online experience that were helpful and features that were not, and we identify student learning strategies that were effective and those (...)
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  32.  66
    Independence Relations Produced by Parameter Values in Causal Models.Richard Scheines - 1990 - Philosophical Topics 18 (2):55-70.
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  33.  9
    Optimizing Student Models for Causality.Benjamin Shih, Kenneth Koedinger & Richard Scheines - unknown
    Complex student models often include key parameters critical to their behavior and effectiveness. For example, one meta-cognitive model of student help-seeking in intelligent tutors includes 15 rules and 10 parameters. We explore whether or not this model can be improved both in accuracy and generalization by using a variety of techniques to select and tune parameters.We show that such techniques are important by demonstrating that the normal method of fitting parameters on an initial data set generalizes poorly to new test (...)
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  34.  51
    Computation and Causation.Richard Scheines - 2002 - In James Moor & Terrell Ward Bynum (eds.), Metaphilosophy. Blackwell. pp. 158-180.
    In 1982, when computers were just becoming widely available, I was a graduate student beginning my work with Clark Glymour on a PhD thesis entitled: “Causality in the Social Sciences.” Dazed and confused by the vast philosophical literature on causation, I found relative solace in the clarity of Structural Equation Models (SEMs), a form of statistical model used commonly by practicing sociologists, political scientists, etc., to model causal hypotheses with which associations among measured variables might be explained. The statistical literature (...)
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  35.  12
    Computation and Causation.Richard Scheines - 2002 - Metaphilosophy 33 (1‐2):158-180.
  36.  20
    Time and Attention: Students, Sessions, and Tasks.Andrew Arnold, Richard Scheines, Joseph E. Back & Bill Jerome - unknown
    Students in two classes in the fall of 2004 making extensive use of online courseware were logged as they visited over 500 different “learning pages” which varied in length and in difficulty. We computed the time spent on each page by each student during each session they were logged in. We then modeled the time spent for a particular visit as a function of the page itself, the session, and the student. Surprisingly, the average time a student spent on learning (...)
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  37.  5
    Student Profiling From Tutoring System Log Data: When Do Multiple Graphical Representations Matter?Ryan Carlson, Konstantin Genin, Martina A. Rau & Richard Scheines - unknown
    We analyze log-data generated by an experiment with Mathtutor, an intelligent tutoring system for fractions. The experiment compares the educational effectiveness of instruction with single and multiple graphical representations. We extract the error-making and hint-seeking behaviors of each student to characterize their learning strategy. Using an expectation-maximization approach, we cluster the students by their strategic profile. We find that a) experimental condition and learning outcome are clearly associated b) experimental condition and learning strategy are not, and c) almost all of (...)
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  38.  4
    Comorbid Science?David Danks, Stephen Fancsali, Clark Glymour & Richard Scheines - 2010 - Behavioral and Brain Sciences 33 (2-3):153 - 155.
    We agree with Cramer et al.'s goal of the discovery of causal relationships, but we argue that the authors' characterization of latent variable models (as deployed for such purposes) overlooks a wealth of extant possibilities. We provide a preliminary analysis of their data, using existing algorithms for causal inference and for the specification of latent variable models.
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  39.  8
    Tis Better to Construct Than to Receive? The Effects of Diagram Tools on Causal Reasoning.Matthew Easterday, Vincent Aleven & Richard Scheines - unknown
    Previous research on the use of diagrams for argumentation instruction has highlighted, but not conclusively demonstrated, their potential benefits. We examine the relative benefits of using diagrams and diagramming tools to teach causal reasoning about public policy. Sixty-three Carnegie Mellon University students were asked to analyze short policy texts using either: 1) text only, 2) text and a pre-made, correct diagram representing the causal claims in the text, or 3) text and a diagramming tool with which to construct their own (...)
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  40.  71
    Causal Modeling with the TETRAD Program.Clark Glymour & Richard Scheines - 1986 - Synthese 68 (1):37 - 63.
    Drawing substantive conclusions from linear causal models that perform acceptably on statistical tests is unreasonable if it is not known how alternatives fare on these same tests. We describe a computer program, TETRAD, that helps to search rapidly for plausible alternatives to a given causal structure. The program is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence. We describe these principles, discuss how TETRAD employs them, and argue that these principles make TETRAD an effective tool. (...)
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  41.  14
    Exploring Causal Structure with the TETRAD Program.Clark Glymour, Richard Scheines & Peter Spirtes - unknown
  42.  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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  43.  9
    Genetic Algorithm Search Over Causal Models.Shane Harwood & Richard Scheines - unknown
    Shane Harwood and Richard Scheines. Genetic Algorithm Search Over Causal Models.
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  44.  22
    Learning Linear Causal Structure Equation Models with Genetic Algorithms.Shane Harwood & Richard Scheines - unknown
    Shane Harwood and Richard Scheines. Learning Linear Causal Structure Equation Models with Genetic Algorithms.
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  45.  22
    Single Mother's Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study.Aurora P. Jackson & Richard Scheines - unknown
    Aurora P. Jackson and Richard Scheines. Single Mother's Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study.
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  46.  13
    Does Representational Understanding Enhance Fluency – Or Vice Versa? Searching for Mediation Models.Martina A. Rau, Richard Scheines, Vincent Aleven & Nikol Rummel - unknown
    Conceptual understanding of representations and fluency in using representations are important aspects of expertise. However, little is known about how these competencies interact: does representational understanding facilitate learning of fluency, or does fluency enhance learning of representational understanding? We analyze log data obtained from an experiment that investigates the effects of intelligent tutoring systems support for understanding and fluency in connection-making between fractions representations. The experiment shows that instructional support for both representational understanding and fluency are needed for students to (...)
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  47.  7
    Searching for Variables and Models to Investigate Mediators of Learning From Multiple Representations.Martina A. Rau & Richard Scheines - unknown
    Although learning from multiple representations has been shown to be effective in a variety of domains, little is known about the mechanisms by which it occurs. We analyzed log data on error-rate, hint-use, and time-spent obtained from two experiments with a Cognitive Tutor for fractions. The goal of the experiments was to compare learning from multiple graphical representations of fractions to learning from a single graphical representation. Finding that a simple statistical model did not fit data from either experiment, we (...)
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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.  5
    An Experimental Comparison of Alternative Proof Construction Environments.Richard Scheines & Wilfried Sieg - unknown
    : "In this paper we compare computerized environments in which students complete proof construction exercises in formal logic. Afterbeing given a pretest for logical aptitude, three matched groups were presented identical course material on logic for approximately five weeks by a computer. During the treatment, all students were required to complete several hundred proof construction exercises. The three groups did the exercises and the midterm in different environments. The group with a more sophisticated interface performed better on the midterm. Nearly (...)
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  50.  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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