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  1. Econometrics and Reichenbach's Principle.Sean Muller - unknown
    Reichenbach's 'principle of the common cause' is a foundational assumption of some important recent contributions to quantitative social science methodology but no similar principle appears in econometrics. Reiss (2005) has argued that the principle is necessary for instrumental variables methods in econometrics, and Pearl (2009) builds a framework using it that he proposes as a means of resolving an important methodological dispute among econometricians. We aim to show, through analysis of the main problem instrumental variables methods are used to resolve, (...)
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  • What is Right with 'Bayes Net Methods' and What is Wrong with 'Hunting Causes and Using Them'?Clark Glymour - 2010 - British Journal for the Philosophy of Science 61 (1):161-211.
    Nancy Cartwright's recent criticisms of efforts and methods to obtain causal information from sample data using automated search are considered. In addition to reviewing that effort, I argue that almost all of her criticisms are false and rest on misreading, overgeneralization, or neglect of the relevant literature.
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  • Jon Williamson Bayesian Nets and Causality.Clark Glymour - 2009 - British Journal for the Philosophy of Science 60 (4):849-855.
  • What’s Wrong With Our Theories of Evidence?Julian Reiss - 2014 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 29 (2):283-306.
    This paper surveys and critically assesses existing theories of evidence with respect to four desiderata. A good theory of evidence should be both a theory of evidential support, and of warrant, it should apply to the non-ideal cases in which scientists typically find themselves, and it should be ‘descriptively adequate’, i.e., able to adequately represent typical episodes of evidentiary reasoning. The theories surveyed here—Bayesianism, hypotheticodeductivism,satisfaction theories, error statistics as well as Achinstein’s and Cartwright’s theories—are all found wanting in important respects. (...)
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  • 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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  • What's Wrong With Our Theories of Evidence?Julian Reiss - 2014 - Theoria : An International Journal for Theory, History and Fundations of Science 29 (2):283-306.
    This paper reviews all major theories of evidence such as the Bayesian theory, hypothetico-deductivism, satisfaction theories, error-statistics, Achinstein's explanationist theory and Cartwright's argument theory. All these theories fail to take adequate account of the context in which a hypothesis is established and used. It is argued that the context of an inquiry determines important facts about what evidence is, and how much and what kind has to be collected to establish a hypothesis for a given purpose.
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  • Constructing Variables That Support Causal Inference.Stephen E. Fancsali - unknown
  • 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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  • Venetian Sea Levels, British Bread Prices and the Principle of the Common Cause: A Reassessment.Iñaki San Pedro - 2011 - In H. de Regt, S. Okasha & S. Hartmann (eds.), EPSA Philosophy of Science: Amsterdam 2009. Springer. pp. 341-354.
    It is still a controversial issue whether Reichenbach’s Principle of the Common Cause (RPCC) is a sound method for causal inference. In fact, the status of the principle has been a subject of intense philosophical debate. An extensive literature has been thus generated both with arguments in favor and against the adequacy of the principle. A remarkable argument against the principle, first proposed by Elliott Sober (Sober, 1987, 2001), consists on a counterexample which involves corelations between bread prices in Britain (...)
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  • Causation, Measurement Relevance and No-Conspiracy in EPR.Iñaki San Pedro - 2012 - European Journal for Philosophy of Science 2 (1):137-156.
    In this paper I assess the adequacy of no-conspiracy conditions employed in the usual derivations of the Bell inequality in the context of EPR correlations. First, I look at the EPR correlations from a purely phenomenological point of view and claim that common cause explanations of these cannot be ruled out. I argue that an appropriate common cause explanation requires that no-conspiracy conditions are re-interpreted as mere common cause-measurement independence conditions. In the right circumstances then, violations of measurement independence need (...)
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  • Introduction to the Epistemology of Causation.Frederick Eberhardt - 2009 - Philosophy Compass 4 (6):913-925.
    This survey presents some of the main principles involved in discovering causal relations. They belong to a large array of possible assumptions and conditions about causal relations, whose various combinations limit the possibilities of acquiring causal knowledge in different ways. How much and in what detail the causal structure can be discovered from what kinds of data depends on the particular set of assumptions one is able to make. The assumptions considered here provide a starting point to explore further the (...)
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  • Making Time Stand Still: A Response to Sober's Counter-Example to the Principle of the Common Cause.Daniel Steel - 2003 - British Journal for the Philosophy of Science 54 (2):309-317.
    In a recent article, Elliot Sober responds to challenges to a counter-example that he posed some years earlier to the Principle of the Common Cause (PCC). I agree that Sober has indeed produced a genuine counter-example to the PCC, but argue against the methodological moral that Sober wishes to draw from it. Contrary to Sober, I argue that the possibility of exceptions to the PCC does not undermine its status as a central assumption for methods that endeavor to draw causal (...)
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  • Atomicity and Causal Completeness.Zalán Gyenis & Miklós Rédei - 2014 - Erkenntnis 79 (S3):1-15.
    The role of measure theoretic atomicity in common cause closedness of general probability theories with non-distributive event structures is raised and investigated. It is shown that if a general probability space is non-atomic then it is common cause closed. Conditions are found that entail that a general probability space containing two atoms is not common cause closed but it is common cause closed if it contains only one atom. The results are discussed from the perspective of the Common Cause Principle.
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  • The Ontological Status of Shocks and Trends in Macroeconomics.Kevin D. Hoover - 2015 - Synthese 192 (11):3509-3532.
    Modern empirical macroeconomic models, known as structural autoregressions (SVARs) are dynamic models that typically claim to represent a causal order among contemporaneously valued variables and to merely represent non-structural (reduced-form) co-occurence between lagged variables and contemporaneous variables. The strategy is held to meet the minimal requirements for identifying the residual errors in particular equations in the model with independent, though otherwise not directly observable, exogenous causes (“shocks”) that ultimately account for change in the model. In nonstationary models, such shocks accumulate (...)
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