Search results for 'causal reasoning' (try it on Scholar)

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  1.  16
    York Hagmayer & Magda Osman (2012). From Colliding Billiard Balls to Colluding Desperate Housewives: Causal Bayes Nets as Rational Models of Everyday Causal Reasoning. Synthese 189 (S1):17-28.
    Many of our decisions pertain to causal systems. Nevertheless, only recently has it been claimed that people use causal models when making judgments, decisions and predictions, and that causal Bayes nets allow us to formally describe these inferences. Experimental research has been limited to simple, artificial problems, which are unrepresentative of the complex dynamic systems we successfully deal with in everyday life. For instance, in social interactions, we can explain the actions of other's on the fly and (...)
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  2. Christoph Hoerl (2011). Causal Reasoning. 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, (...)
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  3.  9
    York Hagmayer (2016). Causal Bayes Nets as Psychological Theories of Causal Reasoning: Evidence From Psychological Research. Synthese 193 (4):1107-1126.
    Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal structures, to induce causal structure from data and to derive predictions. Causal Bayes nets have been used as psychological theories in at least two ways. They were used as rational, computational models of causal reasoning and they were used as formal models of mental causal models. A crucial assumption made by them is the Markov (...)
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  4.  7
    Bob Rehder (2003). Categorization as Causal Reasoning⋆. Cognitive Science 27 (5):709-748.
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  5. Jonathan Y. Tsou (2012). Intervention, Causal Reasoning, and the Neurobiology of Mental Disorders: Pharmacological Drugs as Experimental Instruments. Studies in History and Philosophy of Science Part C 43 (2):542-551.
    In psychiatry, pharmacological drugs play an important experimental role in attempts to identify the neurobiological causes of mental disorders. Besides being developed in applied contexts as potential treatments for patients with mental disorders, pharmacological drugs play a crucial role in research contexts as experimental instruments that facilitate the formulation and revision of neurobiological theories of psychopathology. This paper examines the various epistemic functions that pharmacological drugs serve in the discovery, refinement, testing, and elaboration of neurobiological theories of mental disorders. I (...)
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  6.  10
    Matt Farr, Review of Mathias Frisch's Causal Reasoning in Physics. [REVIEW]
    Review of 'Causal Reasoning in Physics' by Mathias Frisch for British Journal for Philosophy of Science.
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  7.  47
    D. Sobel (2004). Children?S Causal Inferences From Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers. Cognitive Science 28 (3):303-333.
  8. Jonathan Phillips & Alex Shaw (2014). Manipulating Morality: Third‐Party Intentions Alter Moral Judgments by Changing Causal Reasoning. Cognitive Science 38 (8):1320-1347.
    The present studies investigate how the intentions of third parties influence judgments of moral responsibility for other agents who commit immoral acts. Using cases in which an agent acts under some situational constraint brought about by a third party, we ask whether the agent is blamed less for the immoral act when the third party intended for that act to occur. Study 1 demonstrates that third-party intentions do influence judgments of blame. Study 2 finds that third-party intentions only influence moral (...)
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  9.  15
    Richard Scheines, Matt Easterday & David Danks (2007). Teaching the Normative Theory of Causal Reasoning. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press 119--38.
    There is now substantial agreement about the representational component of a normative theory of causal reasoning: Causal Bayes Nets. There is less agreement about a normative theory of causal discovery from data, either computationally or cognitively, and almost no work investigating how teaching the Causal Bayes Nets representational apparatus might help individuals faced with a causal learning task. Psychologists working to describe how naïve participants represent and learn causal structure from data have focused (...)
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  10.  8
    Alison Gopnik (2004). Children's Causal Inferences From Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers. Cognitive Science 28 (3):303-333.
    Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector”, a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine’s (...)
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  11.  46
    Mathias Frisch (2014). Causal Reasoning in Physics. Cambridge University Press.
    Much has been written on the role of causal notions and causal reasoning in the so-called 'special sciences' and in common sense. But does causal reasoning also play a role in physics? Mathias Frisch argues that, contrary to what influential philosophical arguments purport to show, the answer is yes. Time-asymmetric causal structures are as integral a part of the representational toolkit of physics as a theory's dynamical equations. Frisch develops his argument partly through a (...)
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  12.  9
    James Woodward, Causal Reasoning: Philosophy and Experiment.
    This paper explores some interactions between normative/ philosophical/theoretical theorizing about causation and empirical research into causal reasoning and judgment of the sort conducted by psychologists and others. I attempt to extract some general morals regarding the kinds of interactions between the empirical and the more traditionally philosophical that in my experience have been most fruitful. I also compare the experimental work on which I focus with some of the research strategies employed in experimental philosophy.
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  13.  4
    Jiji Zhang, Causal Reasoning with Ancestral Graphical Models.
    Causal reasoning is primarily concerned with what would happen to a system under external interventions. In particular, we are often interested in predicting the probability distribution of some random variables that would result if some other variables were forced to take certain values. One prominent approach to tackling this problem is based on causal Bayesian networks, using directed acyclic graphs as causal diagrams to relate post-intervention probabilities to pre-intervention probabilities that are estimable from observational data. However, (...)
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  14.  17
    Deborah Boyle (2014). The Ways of the Wise: Hume's Rules of Causal Reasoning. Hume Studies 38 (2):157-182.
    In Hume’s own day, and for nearly two hundred years after that, readers interested in his account of causal reasoning tended to focus on the skeptical implications of that account. For example, in his 1757 View of the Principal Deistical Writers of the Last and Present Century, John Leland characterized Hume as “endeavouring to destroy all reasoning, from causes to effects, or from effects to causes.”1 According to this sort of reading, as Louis Loeb describes it, “there (...)
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  15.  39
    Dominick A. Rizzi (1994). Causal Reasoning and the Diagnostic Process. Theoretical Medicine and Bioethics 15 (3):315-333.
    Background: Causal reasoning as a way to make a diagnosis seems convincing. Modern medicine depends on the search for causes of disease and it seems fair to assert that such knowledge is employed in diagnosis. Causal reasoning as it has been presented neglects to some extent the conception of multifactorial disease causes. Goal: The purpose of this paper is to analyze aspects of causation relevant for discussing causal reasoning in a diagnostic context.
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  16.  19
    York Hagmayer & Ralf Mayrhofer (2013). Hierarchical Bayesian Models as Formal Models of Causal Reasoning. Argument and Computation 4 (1):36 - 45.
    (2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 36-45. doi: 10.1080/19462166.2012.700321.
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  17.  1
    James Woodward, Normative Theory and Descriptive Psychology in Understanding Causal Reasoning: The Role of Interventions and Invariance.
    This paper, like its companion explores some ways in which, on the one hand, normative theorizing about causation and causal reasoning and, on the other, empirical psychological investigations into causal cognition can be mutually illuminating. The topics considered include the connection between causal claims and claims about the outcomes of interventions and the various ways that invariance claims figure in causal judgment.
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  18.  1
    Matthew Easterday, Vincent Aleven & Richard Scheines, Tis Better to Construct Than to Receive? The Effects of Diagram Tools on Causal Reasoning.
    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 (...)
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  19.  78
    James M. Joyce (2010). Causal Reasoning and Backtracking. Philosophical Studies 147 (1):139 - 154.
    I argue that one central aspect of the epistemology of causation, the use of causes as evidence for their effects, is largely independent of the metaphysics of causation. In particular, I use the formalism of Bayesian causal graphs to factor the incremental evidential impact of a cause for its effect into a direct cause-to-effect component and a backtracking component. While the “backtracking” evidence that causes provide about earlier events often obscures things, once we our restrict attention to the cause-to-effect (...)
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  20.  11
    York Hagmayer, Steven A. Sloman, David A. Lagnado & Michael R. Waldmann (2007). Causal Reasoning Through Intervention. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press
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  21.  47
    Mathias Frisch (2009). 'The Most Sacred Tenet'? Causal Reasoning in Physics. British Journal for the Philosophy of Science 60 (3):459-474.
    According to a view widely held among philosophers of science, the notion of cause has no legitimate role to play in mature theories of physics. In this paper I investigate the role of what physicists themselves identify as causal principles in the derivation of dispersion relations. I argue that this case study constitutes a counterexample to the popular view and that causal principles can function as genuine factual constraints. IntroductionCausality and Dispersion RelationsNorton's SkepticismConclusion.
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  22.  6
    Henry M. Wellman & David Liu (2007). Causal Reasoning as Informed by the Early Development of Explanations. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press 261--279.
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  23.  44
    Isabelle Drouet (2012). Causal Reasoning, Causal Probabilities, and Conceptions of Causation. Studies in History and Philosophy of Science Part C 43 (4):761-768.
    The present paper deals with the tools that can be used to represent causation and to reason about it and, specifically, with their diversity. It focuses on so-called “causal probabilities”—that is, probabilities of effects given one of their causes—and critically surveys a recent paper in which Joyce argues that the values of these probabilities do not depend on one’s conception of causation. I first establish a stronger independence claim: I show that the very definition of causal probabilities is (...)
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  24.  3
    Alex H. Taylor & Nicola S. Clayton (2012). Evidence From Convergent Evolution and Causal Reasoning Suggests That Conclusions on Human Uniqueness May Be Premature. Behavioral and Brain Sciences 35 (4):241-242.
    We agree with Vaesen that there is evidence for cognitive differences between humans and other primates. However, it is too early to draw firm conclusions about the uniqueness of the cognitive mechanisms underlying human tool use. Tests of causal understanding are in their infancy, as is the study of animals more distantly related to humans.
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  25.  29
    Paul L. Harris, Tim German & Patrick Mills (1996). Children's Use of Counterfactual Thinking in Causal Reasoning. Cognition 61 (3):233-259.
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  26.  7
    Ziva Kunda, Dale T. Miller & Theresa Claire (1990). Combining Social Concepts: The Role of Causal Reasoning. Cognitive Science 14 (4):551-577.
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  27.  92
    James Woodward (2011). Psychological Studies of Causal and Counterfactual Reasoning. In Christoph Hoerl, Teresa McCormack & Sarah R. Beck (eds.), Understanding Counterfactuals, Understanding Causation. Oxford University Press 16.
    Counterfactual theories of causation of the sort presented in Mackie, 1974, and Lewis, 1973 are a familiar part of the philosophical landscape. Such theories are typically advanced primarily as accounts of the metaphysics of causation. But they also raise empirical psychological issues concerning the processes and representations that underlie human causal reasoning. For example, do human subjects internally represent causal claims in terms of counterfactual judgments and when they engage in causal reasoning, does this involves (...)
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  28.  3
    Barbara A. Spellman, Alexandra P. Kincannon & Stephen J. Stose (2005). The Relation Between Counterfactual and Causal Reasoning. In David R. Mandel, Denis J. Hilton & Patrizia Catellani (eds.), The Psychology of Counterfactual Thinking. Routledge 28--43.
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  29.  14
    Niki Verschueren, Walter Schaeken & G. (2005). A Dual-Process Specification of Causal Conditional Reasoning. Thinking and Reasoning 11 (3):239 – 278.
    There are two accounts describing causal conditional reasoning: the probabilistic and the mental models account. According to the probabilistic account, the tendency to accept a conclusion is related to the probability by which cause and effect covary. According to the mental models account, the tendency to accept a conclusion relates to the availability of counterexamples. These two accounts are brought together in a dual-process theory: It is argued that the probabilistic reasoning process can be considered as a (...)
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  30.  9
    K. I. Manktelow & N. Fairley (2000). Superordinate Principles in Reasoning with Causal and Deontic Conditionals. Thinking and Reasoning 6 (1):41 – 65.
    We propose that the pragmatic factors that mediate everyday deduction, such as alternative and disabling conditions (e.g. Cummins et al., 1991) and additional requirements (Byrne, 1989) exert their effects on specific inferences because of their perceived relevance to more general principles, which we term SuperPs. Support for this proposal was found first in two causal inference experiments, in which it was shown that specific inferences were mediated by factors that are relevant to a more general principle, while the same (...)
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  31.  3
    Benjamin Kuipers & Jerome P. Kassirer (1984). Causal Reasoning in Medicine: Analysis of a Protocol. Cognitive Science 8 (4):363-385.
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  32. S. A. Sloman & Philip M. Fernbach (2008). The Value of Rational Analysis: An Assessment of Causal Reasoning and Learning. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford 486--500.
  33.  16
    Stephane Quinn & Henry Markovits (2002). Conditional Reasoning with Causal Premises: Evidence for a Retrieval Model. Thinking and Reasoning 8 (3):179 – 191.
    This study examined the hypothesis that a key process in conditional reasoning with concrete premises involves on-line retrieval of information about potential alternate antecedents. Participants were asked to solve reasoning problems with causal conditional premises (If cause P then effect Q). These premises were inserted into short contexts. The availability of potential alternatives was varied from one context to another by adding statements that explicitly invalidated one or more of these alternatives (i.e., other causes that lead to (...)
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  34.  9
    Momme von Sydow, Björn Meder & York Hagmayer (2009). A Transitivity Heuristic of Probabilistic Causal Reasoning. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
  35. Jonathan A. Fugelsang & Kevin N. Dunbar (2006). A Cognitive Neuroscience Framework for Understanding Causal Reasoning and the Law. In Semir Zeki & Oliver Goodenough (eds.), Law and the Brain. OUP Oxford 157--166.
     
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  36. A. Spellman Barbara, P. Kincannon Alexandra & J. Stose Stephen (2005). The Relation Between Counterfactual and Causal Reasoning. In David R. Mandel, Denis J. Hilton & Patrizia Catellani (eds.), The Psychology of Counterfactual Thinking. Routledge
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  37.  39
    John Norton, Is There an Independent Principle of Causality in Physics? A Comment on Matthias Frisch, 'Causal Reasoning in Physics.'.
    Earlier version on philsci-archive.pitt.edu; latest version.
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  38.  4
    Barbara A. Spellman & Dieynaba G. Ndiaye (2007). On the Relation Between Counterfactual and Causal Reasoning. Behavioral and Brain Sciences 30 (5-6):466-467.
    We critique the distinction Byrne makes between strong causes and enabling conditions, and its implications, on both theoretical and empirical grounds. First, we believe that the difference is psychological, not logical. Second, we disagree that there is a strict Third, we disagree that it is easier for people to generate causes than counterfactuals.
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  39.  8
    Chen-Fang Tsai (2002). Genetic Algorithms with Temporal Causal Reasoning for AGENT-BASED Supply Chain Management. Aletheia 18 (2):63-78.
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  40.  19
    Ira Brooks‐Walsh & Edmund V. Sullivan (1973). The Relationship Between Moral Judgment, Causal Reasoning and General Reasoning. Journal of Moral Education 2 (2):131-136.
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  41.  22
    Sieghard Beller & Gregory Kuhnm (2007). What Causal Conditional Reasoning Tells Us About People's Understanding of Causality. Thinking and Reasoning 13 (4):426 – 460.
    Causal conditional reasoning means reasoning from a conditional statement that refers to causal content. We argue that data from causal conditional reasoning tasks tell us something not only about how people interpret conditionals, but also about how they interpret causal relations. In particular, three basic principles of people's causal understanding emerge from previous studies: the modal principle, the exhaustive principle, and the equivalence principle. Restricted to the four classic conditional inferences—Modus Ponens, Modus (...)
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  42.  2
    Robin Im Dunbar (2000). Causal Reasoning, Mental Rehearsal, and the Evolution of Primate Cognition. In Celia Heyes & Ludwig Huber (eds.), The Evolution of Cognition. MIT Press
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  43.  4
    Fritz Ringer (1989). Causal Analysis in Historical Reasoning. History and Theory 28 (2):154-172.
    Contemporary analytical philosophy has not provided historians with an adequate account of their causal reasoning. Attempts to apply the laws of scientific explanation to history have occasioned an artificial split between historical interpretation and historical explanation. The lawlike generalizations of the natural sciences are both perfectly universal and perfectly delimited, whereas the typical generalizations of the historian are imperfectly universal and imperfectly delimited. In historical analysis, a particular development is hypothetically posited as the ordinary course of events, or (...)
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  44.  7
    Jaakko Kuorikoski (2011). Variations in Causal Reasoning Causality and Causal Modelling in the Social Sciences: Measuring Variations. Journal of Economic Methodology 18 (3):301-305.
  45. Isabelle Drouet (2012). Causal Reasoning, Causal Probabilities, and Conceptions of Causation. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (4):761-768.
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  46. Jonathan Fugelsang & Dunbar & Kevin (2006). A Cognitive Neuroscience Framework for Understanding Causal Reasoning and the Law. In Semir Zeki & Oliver Goodenough (eds.), Law and the Brain. OUP Oxford
     
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  47. Steven Sloman & Fernbach & M. Philip (2008). The Value of Rational Analysis: An Assessment of Causal Reasoning and Learning. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford
     
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  48. Vl Patel, Gj Groen & As Chawla (1988). Causal Reasoning About Complex Physiological-Mechanisms by Novices. Bulletin of the Psychonomic Society 26 (6):491-491.
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  49. C. Kenneth Waters & James Woodward (eds.) (forthcoming). Philosophical Perspectives on Causal Reasoning in Biology. University of Minnesota Press.
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  50.  31
    Morteza Dehghani, Rumen Iliev & Stefan Kaufmann (2012). Causal Explanation and Fact Mutability in Counterfactual Reasoning. Mind and Language 27 (1):55-85.
    Recent work on the interpretation of counterfactual conditionals has paid much attention to the role of causal independencies. One influential idea from the theory of Causal Bayesian Networks is that counterfactual assumptions are made by intervention on variables, leaving all of their causal non-descendants unaffected. But intervention is not applicable across the board. For instance, backtracking counterfactuals, which involve reasoning from effects to causes, cannot proceed by intervention in the strict sense, for otherwise they would be (...)
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