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  1. On Dispositional Masks.Matthew Turyn - forthcoming - Synthese:1-22.
    Dispositions can be masked: some state of affairs might obtain which would prevent an entity from displaying the manifestation characteristic of its disposition. Yet discussions of masks overlook a number of key problems, chief among them the probabilistic nature of many dispositional masks. In this paper, I highlight the manner in which past analyses of dispositional masks have been unable to solve the problem of masks. I propose an analysis of dispositional masks which focuses on this and a number of (...)
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  • The Higgs Discovery as a Diagnostic Causal Inference.Adrian Wüthrich - 2017 - Synthese 194 (2).
    I reconstruct the discovery of the Higgs boson by the ATLAS collaboration at CERN as the application of a series of inferences from effects to causes. I show to what extent such diagnostic causal inferences can be based on well established knowledge gained in previous experiments. To this extent, causal reasoning can be used to infer the existence of entities, rather than just causal relationships between them. The resulting account relies on the principle of causality, attributes only a heuristic role (...)
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  • Bayesian Networks and Causal Ecumenism.David Kinney - forthcoming - Erkenntnis:1-26.
    Proponents of various causal exclusion arguments claim that for any given event, there is often a unique level of granularity at which that event is caused. Against these causal exclusion arguments, causal ecumenists argue that the same event or phenomenon can be caused at multiple levels of granularity. This paper argues that the Bayesian network approach to representing the causal structure of target systems is consistent with causal ecumenism. Given the ubiquity of Bayesian networks as a tool for representing causal (...)
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  • Free Will, Control, and the Possibility to Do Otherwise From a Causal Modeler’s Perspective.Alexander Gebharter, Maria Sekatskaya & Gerhard Schurz - forthcoming - Erkenntnis:1-18.
    Strong notions of free will are closely connected to the possibility to do otherwise as well as to an agent's ability to causally influence her environment via her decisions controlling her actions. In this paper we employ techniques from the causal modeling literature to investigate whether a notion of free will subscribing to one or both of these requirements is compatible with naturalistic views of the world such as non-reductive physicalism to the background of determinism and indeterminism. We argue that (...)
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  • Causation, Exclusion, and the Special Sciences.Panu Raatikainen - 2010 - Erkenntnis 73 (3):349-363.
    The issue of downward causation (and mental causation in particular), and the exclusion problem is discussed by taking into account some recent advances in the philosophy of science. The problem is viewed from the perspective of the new interventionist theory of causation developed by Woodward. It is argued that from this viewpoint, a higher-level (e.g., mental) state can sometimes truly be causally relevant, and moreover, that the underlying physical state which realizes it may fail to be such.
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  • Facts, Conventions, and the Levels of Selection.Pierrick Bourrat - 2021 - Cambridge, UK: Cambridge University Press.
    Debates concerning the units and levels of selection have persisted for over fifty years. One major question in this literature is whether units and levels of selection are genuine, in the sense that they are objective features of the world, or merely reflect the interests and goals of an observer. Scientists and philosophers have proposed a range of answers to this question. This Element introduces this literature and proposes a novel contribution. It defends a realist stance and offers a way (...)
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  • Causal Reasoning.Christoph Hoerl - 2011 - Philosophical Studies 152 (2):167-179.
    The main focus of this paper is the question as to what it is for an individual to think of her environment in terms of a concept of causation, or causal concepts, in contrast to some more primitive ways in which an individual might pick out or register what are in fact causal phenomena. I show how versions of this question arise in the context of two strands of work on causation, represented by Elizabeth Anscombe and Christopher Hitchcock, respectively. I (...)
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  • Non-Bayesian Inference: Causal Structure Trumps Correlation.Bénédicte Bes, Steven Sloman, Christopher G. Lucas & Éric Raufaste - 2012 - Cognitive Science 36 (7):1178-1203.
    The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of (...)
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  • Must Philosophy Be Constrained?: Edouard Machery: Philosophy Within its Proper Bounds. Oxford: Oxford University Press, 2017, 217pp, £40.00HB. [REVIEW]Anna Drożdżowicz, Pierre Saint-Germier & Samuel Schindler - 2018 - Metascience 27 (3):469-475.
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  • On the Relationship Between Individual and Population Health.Onyebuchi A. Arah - 2009 - Medicine, Health Care and Philosophy 12 (3):235-244.
    The relationship between individual and population health is partially built on the broad dichotomization of medicine into clinical medicine and public health. Potential drawbacks of current views include seeing both individual and population health as absolute and independent concepts. I will argue that the relationship between individual and population health is largely relative and dynamic. Their interrelated dynamism derives from a causally defined life course perspective on health determination starting from an individual’s conception through growth, development and participation in the (...)
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  • Verisimilitude: A Causal Approach.Robert Northcott - 2013 - Synthese 190 (9):1471-1488.
    I present a new definition of verisimilitude, framed in terms of causes. Roughly speaking, according to it a scientific model is approximately true if it captures accurately the strengths of the causes present in any given situation. Against much of the literature, I argue that any satisfactory account of verisimilitude must inevitably restrict its judgments to context-specific models rather than general theories. We may still endorse—and only need—a relativized notion of scientific progress, understood now not as global advance but rather (...)
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  • The Causal Autonomy of the Special Sciences.Peter Menzies & Christian List - 2010 - In Cynthia McDonald & Graham McDonald (eds.), Emergence in Mind. Oxford University Press. pp. 108-129.
    The systems studied in the special sciences are often said to be causally autonomous, in the sense that their higher-level properties have causal powers that are independent of the causal powers of their more basic physical properties. This view was espoused by the British emergentists, who claimed that systems achieving a certain level of organizational complexity have distinctive causal powers that emerge from their constituent elements but do not derive from them. More recently, non-reductive physicalists have espoused a similar view (...)
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  • Simpson's Paradox and Causality.Prasanta S. Bandyopadhyay, Mark Greenwood, Don Dcruz & Venkata Raghavan - 2015 - American Philosophical Quarterly 52 (1):13-25.
    There are three questions associated with Simpson’s Paradox (SP): (i) Why is SP paradoxical? (ii) What conditions generate SP?, and (iii) What should be done about SP? By developing a logic-based account of SP, it is argued that (i) and (ii) must be divorced from (iii). This account shows that (i) and (ii) have nothing to do with causality, which plays a role only in addressing (iii). A counterexample is also presented against the causal account. Finally, the causal and logic-based (...)
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  • How Simulations Fail.Patrick Grim, Robert Rosenberger, Adam Rosenfeld, Brian Anderson & Robb E. Eason - 2013 - Synthese 190 (12):2367-2390.
    ‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build on a structural (...)
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  • The Risk GP Model: The Standard Model of Prediction in Medicine.Jonathan Fuller & Luis J. Flores - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 54:49-61.
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  • Antireductionist Interventionism.Reuben Stern & Benjamin Eva - forthcoming - British Journal for the Philosophy of Science.
    Kim’s causal exclusion argument purports to demonstrate that the non-reductive physicalist must treat mental properties (and macro-level properties in general) as causally inert. A number of authors have attempted to resist Kim’s conclusion by utilizing the conceptual resources of Woodward’s (2005) interventionist conception of causation. The viability of these responses has been challenged by Gebharter (2017a), who argues that the causal exclusion argument is vindicated by the theory of causal Bayesian networks (CBNs). Since the interventionist conception of causation relies crucially (...)
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  • Neutral Monism Reconsidered.Erik C. Banks - 2010 - Philosophical Psychology 23 (2):173-187.
    Neutral monism is a position in metaphysics defended by Mach, James, and Russell in the early twentieth century. It holds that minds and physical objects are essentially two different orderings of the same underlying neutral elements of nature. This paper sets out some of the central concepts, theses and the historical background of ideas that inform this doctrine of elements. The discussion begins with the classic neutral monism of Mach, James, and Russell in the first part of the paper, then (...)
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  • The Problem of Granularity for Scientific Explanation.David Kinney - 2019 - Dissertation, London School of Economics and Political Science (LSE)
    This dissertation aims to determine the optimal level of granularity for the variables used in probabilistic causal models. These causal models are useful for generating explanations in a number of scientific contexts. In Chapter 1, I argue that there is rarely a unique level of granularity at which a given phenomenon can be causally explained, thereby rejecting various causal exclusion arguments. In Chapter 2, I consider several recent proposals for measuring the explanatory power of causal explanations, and show that these (...)
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  • Reversing 30 Years of Discussion: Why Causal Decision Theorists Should One-Box.Wolfgang Spohn - 2012 - Synthese 187 (1):95-122.
    The paper will show how one may rationalize one-boxing in Newcomb's problem and drinking the toxin in the Toxin puzzle within the confines of causal decision theory by ascending to so-called reflexive decision models which reflect how actions are caused by decision situations (beliefs, desires, and intentions) represented by ordinary unreflexive decision models.
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  • The Supposed Competition Between Theories of Human Causal Inference.David Danks - 2005 - Philosophical Psychology 18 (2):259 – 272.
    Newsome ((2003). The debate between current versions of covariation and mechanism approaches to causal inference. Philosophical Psychology, 16, 87-107.) recently published a critical review of psychological theories of human causal inference. In that review, he characterized covariation and mechanism theories, the two dominant theory types, as competing, and offered possible ways to integrate them. I argue that Newsome has misunderstood the theoretical landscape, and that covariation and mechanism theories do not directly conflict. Rather, they rely on distinct sets of reliable (...)
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  • Are Newcomb Problems Really Decisions?James M. Joyce - 2006 - Synthese 156 (3):537-562.
    Richard Jeffrey long held that decision theory should be formulated without recourse to explicitly causal notions. Newcomb problems stand out as putative counterexamples to this ‘evidential’ decision theory. Jeffrey initially sought to defuse Newcomb problems via recourse to the doctrine of ratificationism, but later came to see this as problematic. We will see that Jeffrey’s worries about ratificationism were not compelling, but that valid ratificationist arguments implicitly presuppose causal decision theory. In later work, Jeffrey argued that Newcomb problems are not (...)
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  • Causal Exclusion and Causal Bayes Nets.Alexander Gebharter - 2017 - Philosophy and Phenomenological Research 95 (2):353-375.
    In this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments within the theory of causal Bayes nets. I argue that supervenience relations formally behave like causal relations. If this is correct, then it turns out that both versions of the exclusion argument are valid when assuming the causal Markov condition and the causal minimality condition. I also investigate some consequences for the recent discussion of causal exclusion arguments in the light of an interventionist theory (...)
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  • Singular and General Causal Relations: A Mechanist Perspective.Stuart Glennan - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
    My aim in this paper is to make a case for the singularist view from the perspective of a mechanical theory of causation, and to explain what, from this perspective, causal generalizations mean, and what role they play within the mechanical theory.
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  • Abstraction and the Organization of Mechanisms.Arnon Levy & William Bechtel - 2013 - Philosophy of Science 80 (2):241-261.
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  • The Doctrine of Specific Etiology.Lauren N. Ross - 2018 - Biology and Philosophy 33 (5-6):37.
    Modern medicine is often said to have originated with nineteenth century germ theory, which attributed diseases to bacterial contagions. The success of this theory is often associated with an underlying principle referred to as the “doctrine of specific etiology”. This doctrine refers to specificity at the level of disease causation or etiology. While the importance of this doctrine is frequently emphasized in the philosophical, historical, and medical literature, these sources lack a clear account of the types of specificity that it (...)
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  • Learning From Conditionals.Benjamin Eva, Stephan Hartmann & Soroush Rafiee Rad - 2020 - Mind 129 (514):461-508.
    In this article, we address a major outstanding question of probabilistic Bayesian epistemology: how should a rational Bayesian agent update their beliefs upon learning an indicative conditional? A number of authors have recently contended that this question is fundamentally underdetermined by Bayesian norms, and hence that there is no single update procedure that rational agents are obliged to follow upon learning an indicative conditional. Here we resist this trend and argue that a core set of widely accepted Bayesian norms is (...)
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  • Confirmation and Reduction: A Bayesian Account.Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann - 2011 - Synthese 179 (2):321-338.
    Various scientific theories stand in a reductive relation to each other. In a recent article, we have argued that a generalized version of the Nagel-Schaffner model (GNS) is the right account of this relation. In this article, we present a Bayesian analysis of how GNS impacts on confirmation. We formalize the relation between the reducing and the reduced theory before and after the reduction using Bayesian networks, and thereby show that, post-reduction, the two theories are confirmatory of each other. We (...)
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  • Structural Counterfactuals: A Brief Introduction.Judea Pearl - 2013 - Cognitive Science 37 (6):977-985.
    Recent advances in causal reasoning have given rise to a computational model that emulates the process by which humans generate, evaluate, and distinguish counterfactual sentences. Contrasted with the “possible worlds” account of counterfactuals, this “structural” model enjoys the advantages of representational economy, algorithmic simplicity, and conceptual clarity. This introduction traces the emergence of the structural model and gives a panoramic view of several applications where counterfactual reasoning has benefited problem areas in the empirical sciences.
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  • Sufficiency and Necessity Assumptions in Causal Structure Induction.Ralf Mayrhofer & Michael R. Waldmann - 2016 - Cognitive Science 40 (8):2137-2150.
    Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found (...)
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  • Science Without (Parametric) Models: The Case of Bootstrap Resampling.Jan Sprenger - 2011 - Synthese 180 (1):65-76.
    Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make (...)
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  • Uncovering Deterministic Causal Structures: A Boolean Approach.Michael Baumgartner - 2009 - Synthese 170 (1):71-96.
    While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic struc- tures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or—in case of ambiguities—more than one causal structure is (...)
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  • A Combinatorial Solution to Causal Compatibility.Thomas C. Fraser - 2020 - Journal of Causal Inference 8 (1):22-53.
    Within the field of causal inference, it is desirable to learn the structure of causal relationships holding between a system of variables from the correlations that these variables exhibit; a sub-problem of which is to certify whether or not a given causal hypothesis is compatible with the observed correlations. A particularly challenging setting for assessing causal compatibility is in the presence of partial information; i.e. when some of the variables are hidden/latent. This paper introduces the possible worlds framework as a (...)
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  • The Logic of Simpson’s Paradox.Prasanta S. Bandyoapdhyay, Davin Nelson, Mark Greenwood, Gordon Brittan & Jesse Berwald - 2011 - Synthese 181 (2):185 - 208.
    There are three distinct questions associated with Simpson's paradox, (i) Why or in what sense is Simpson's paradox a paradox? (ii) What is the proper analysis of the paradox? (iii) How one should proceed when confronted with a typical case of the paradox? We propose a "formar" answer to the first two questions which, among other things, includes deductive proofs for important theorems regarding Simpson's paradox. Our account contrasts sharply with Pearl's causal (and questionable) account of the first two questions. (...)
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  • Philosophy as Conceptual Engineering: Inductive Logic in Rudolf Carnap's Scientific Philosophy.Christopher F. French - 2015 - Dissertation, University of British Columbia
    My dissertation explores the ways in which Rudolf Carnap sought to make philosophy scientific by further developing recent interpretive efforts to explain Carnap’s mature philosophical work as a form of engineering. It does this by looking in detail at his philosophical practice in his most sustained mature project, his work on pure and applied inductive logic. I, first, specify the sort of engineering Carnap is engaged in as involving an engineering design problem and then draw out the complications of design (...)
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  • Edge Replacement and Nonindependence in Causation.D. Buchanan, J. Tenenbaum & D. Sobel - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  • Inductive Reasoning About Causally Transmitted Properties.Patrick Shafto, Charles Kemp, Elizabeth Baraff Bonawitz, John D. Coley & Joshua B. Tenenbaum - 2008 - Cognition 109 (2):175-192.
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  • Natural-Born Determinists: A New Defense of Causation as Probability-Raising.Robert Northcott - 2010 - Philosophical Studies 150 (1):1-20.
    A definition of causation as probability-raising is threatened by two kinds of counterexample: first, when a cause lowers the probability of its effect; and second, when the probability of an effect is raised by a non-cause. In this paper, I present an account that deals successfully with problem cases of both these kinds. In doing so, I also explore some novel implications of incorporating into the metaphysical investigation considerations of causal psychology.
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  • Probabilistic Models of Cognition: Conceptual Foundations.Nick Chater & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):287-291.
    Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore (...)
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  • Correlations, Deviations and Expectations: The Extended Principle of the Common Cause.Claudio Mazzola - 2013 - Synthese 190 (14):2853-2866.
    The Principle of the Common Cause is usually understood to provide causal explanations for probabilistic correlations obtaining between causally unrelated events. In this study, an extended interpretation of the principle is proposed, according to which common causes should be invoked to explain positive correlations whose values depart from the ones that one would expect to obtain in accordance to her probabilistic expectations. In addition, a probabilistic model for common causes is tailored which satisfies the generalized version of the principle, at (...)
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  • Intuitive Theories as Grammars for Causal Inference.Joshua B. Tenenbaum, Thomas L. Griffiths & Sourabh Niyogi - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 301--322.
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  • The Precautionary Principle Meets the Hill Criteria of Causation.Daniel Steel & Jessica Yu - 2019 - Ethics, Policy and Environment 22 (1):72-89.
    ABSTRACTThis article examines the relationship between the precautionary principle and the well-known Hill criteria of causation. Some have charged that the Hill criteria are anti-precautionary because they are strongly inclined towards false negatives in multi-causal contexts typical of environmental and public health issues. However, we argue that without guidance on how to interpret and weight the criteria, no such claims can be supported. Using a case study of tuberculosis among South African goldmine workers, we consider the implications of different weightings (...)
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  • Modelling Phenomena and Dynamic Logic of Phenomena.Boris Kovalerchuk, Leonid Perlovsky & Gregory Wheeler - 2012 - Journal of Applied Non-Classical Logics 22 (1-2):53-82.
    Modelling a complex phenomenon such as the mind presents tremendous computational complexity challenges. Modelling field theory addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called the Dynamic Logic of Phenomena for model (...)
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  • Are Causal Analysis and System Analysis Compatible Approaches?Federica Russo - 2010 - International Studies in the Philosophy of Science 24 (1):67 – 90.
    In social science, one objection to causal analysis is that the assumption of the closure of the system makes the analysis too narrow in scope, that is, it considers only 'closed' and 'hermetic' systems thus neglecting many other external influences. On the contrary, system analysis deals with complex structures where every element is interrelated with everything else in the system. The question arises as to whether the two approaches can be compatible and whether causal analysis can be integrated into the (...)
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  • The Logic of Simpson’s Paradox.Prasanta S. Bandyoapdhyay, Davin Nelson, Mark Greenwood, Gordon Brittan & Jesse Berwald - 2011 - Synthese 181 (2):185-208.
    There are three distinct questions associated with Simpson’s paradox. Why or in what sense is Simpson’s paradox a paradox? What is the proper analysis of the paradox? How one should proceed when confronted with a typical case of the paradox? We propose a “formal” answer to the first two questions which, among other things, includes deductive proofs for important theorems regarding Simpson’s paradox. Our account contrasts sharply with Pearl’s causal account of the first two questions. We argue that the “how (...)
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  • Conditionals Right and Left: Probabilities for the Whole Family.Stefan Kaufmann - 2009 - Journal of Philosophical Logic 38 (1):1-53.
    The fact that the standard probabilistic calculus does not define probabilities for sentences with embedded conditionals is a fundamental problem for the probabilistic theory of conditionals. Several authors have explored ways to assign probabilities to such sentences, but those proposals have come under criticism for making counterintuitive predictions. This paper examines the source of the problematic predictions and proposes an amendment which corrects them in a principled way. The account brings intuitions about counterfactual conditionals to bear on the interpretation of (...)
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  • Reliability Via Synthetic a Priori: Reichenbach’s Doctoral Thesis on Probability.Frederick Eberhardt - 2011 - Synthese 181 (1):125-136.
    Hans Reichenbach is well known for his limiting frequency view of probability, with his most thorough account given in The Theory of Probability in 1935/1949. Perhaps less known are Reichenbach's early views on probability and its epistemology. In his doctoral thesis from 1915, Reichenbach espouses a Kantian view of probability, where the convergence limit of an empirical frequency distribution is guaranteed to exist thanks to the synthetic a priori principle of lawful distribution. Reichenbach claims to have given a purely objective (...)
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  • The Causal Chain Problem.Michael Baumgartner - 2008 - Erkenntnis 69 (2):201-226.
    This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertinent empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains are one of the most ubiquitous (macroscopic) causal structures is underdetermined by empirical data. It will be argued that even though the chain and its associated common cause model are empirically equivalent there exists an important asymmetry between (...)
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  • A Sufficient Condition for Pooling Data.Frederick Eberhardt - 2008 - Synthese 163 (3):433 - 442.
    We consider the problems arising from using sequences of experiments to discover the causal structure among a set of variables, none of whom are known ahead of time to be an “outcome”. In particular, we present various approaches to resolve conflicts in the experimental results arising from sampling variability in the experiments. We provide a sufficient condition that allows for pooling of data from experiments with different joint distributions over the variables. Satisfaction of the condition allows for an independence test (...)
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  • 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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  • The Modeler in the Crib.Stuart S. Glennan - 2005 - Philosophical Explorations 8 (3):217-227.
    A number of developmental psychologists have argued for a theory they call the theory theory - a theory of cognitive development that suggests that infants and small children make sense of their world by constructing cognitive representations that have many of the attributes of scientific theories. In this paper I argue that there are indeed close parallels between the activities of children and scientists, but that these parallels will be better understood if one recognizes that both scientists and children are (...)
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