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

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  1. 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.score: 90.0
    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. Jerome R. Busemeyer Jennifer S. Trueblood (2012). A Quantum Probability Model of Causal Reasoning. Frontiers in Psychology 3.score: 90.0
    People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these (...)
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  3. Christoph Hoerl (2011). Causal Reasoning. Philosophical Studies 152 (2):167-179.score: 87.0
    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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  4. 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.score: 75.0
    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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  5. Bob Rehder (2003). Categorization as Causal Reasoning⋆. Cognitive Science 27 (5):709-748.score: 75.0
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  6. Alison Gopnik (2004). Children's Causal Inferences From Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers. Cognitive Science 28 (3):303-333.score: 69.0
    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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  7. D. Sobel (2004). Children?S Causal Inferences From Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers. Cognitive Science 28 (3):303-333.score: 66.0
  8. 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.score: 63.0
    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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  9. Dominick A. Rizzi (1994). Causal Reasoning and the Diagnostic Process. Theoretical Medicine and Bioethics 15 (3):315-333.score: 60.0
    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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  10. York Hagmayer & Ralf Mayrhofer (2013). Hierarchical Bayesian Models as Formal Models of Causal Reasoning. Argument and Computation 4 (1):36 - 45.score: 60.0
    (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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  11. Deborah Boyle (2014). The Ways of the Wise: Hume's Rules of Causal Reasoning. Hume Studies 38 (2):157-182.score: 60.0
    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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  12. Matthew Easterday, Vincent Aleven & Richard Scheines, Tis Better to Construct Than to Receive? The Effects of Diagram Tools on Causal Reasoning.score: 60.0
    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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  13. James M. Joyce (2010). Causal Reasoning and Backtracking. Philosophical Studies 147 (1):139 - 154.score: 54.0
    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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  14. Michael Baumgartner (2009). Uncovering Deterministic Causal Structures: A Boolean Approach. Synthese 170 (1):71 - 96.score: 54.0
    While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic structures, 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 (...)
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  15. Teresa McCormack, Stephen Andrew Butterfill, Christoph Hoerl & Patrick Burns (2009). Cue Competition Effects and Young Children's Causal and Counterfactual Inferences. Developmental Psychology 45 (6):1563-1575.score: 54.0
    The authors examined cue competition effects in young children using the blicket detector paradigm, in which objects are placed either singly or in pairs on a novel machine and children must judge which objects have the causal power to make the machine work. Cue competition effects were found in a 5- to 6-year-old group but not in a 4-year-old group. Equivalent levels of forward and backward blocking were found in the former group. Children's counterfactual judgments were subsequently examined by (...)
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  16. Jiji Zhang (2013). A Lewisian Logic of Causal Counterfactuals. Minds and Machines 23 (1):77-93.score: 51.0
    In the artificial intelligence literature a promising approach to counterfactual reasoning is to interpret counterfactual conditionals based on causal models. Different logics of such causal counterfactuals have been developed with respect to different classes of causal models. In this paper I characterize the class of causal models that are Lewisian in the sense that they validate the principles in Lewis’s well-known logic of counterfactuals. I then develop a system sound and complete with respect to this (...)
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  17. Sieghard Beller & Gregory Kuhnm (2007). What Causal Conditional Reasoning Tells Us About People's Understanding of Causality. Thinking and Reasoning 13 (4):426 – 460.score: 51.0
    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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  18. Niki Verschueren, Walter Schaeken & G. (2005). A Dual-Process Specification of Causal Conditional Reasoning. Thinking and Reasoning 11 (3):239 – 278.score: 51.0
    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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  19. Stephane Quinn & Henry Markovits (2002). Conditional Reasoning with Causal Premises: Evidence for a Retrieval Model. Thinking and Reasoning 8 (3):179 – 191.score: 51.0
    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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  20. Deena S. Weisberg & Alison Gopnik (2013). Pretense, Counterfactuals, and Bayesian Causal Models: Why What Is Not Real Really Matters. Cognitive Science 37 (7):1368-1381.score: 51.0
    Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative representation of reality, and keeping this representation separate from reality. In turn, according to causal models accounts, counterfactual reasoning is a crucial tool that children need to plan for (...)
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  21. K. I. Manktelow & N. Fairley (2000). Superordinate Principles in Reasoning with Causal and Deontic Conditionals. Thinking and Reasoning 6 (1):41 – 65.score: 51.0
    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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  22. Mathias Frisch (2009). 'The Most Sacred Tenet'? Causal Reasoning in Physics. British Journal for the Philosophy of Science 60 (3):459-474.score: 48.0
    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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  23. David A. Lagnado (2009). A Causal Framework for Integrating Learning and Reasoning. Behavioral and Brain Sciences 32 (2):211-212.score: 48.0
    Can the phenomena of associative learning be replaced wholesale by a propositional reasoning system? Mitchell et al. make a strong case against an automatic, unconscious, and encapsulated associative system. However, their propositional account fails to distinguish inferences based on actions from those based on observation. Causal Bayes networks remedy this shortcoming, and also provide an overarching framework for both learning and reasoning. On this account, causal representations are primary, but associative learning processes are not excluded a (...)
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  24. Morteza Dehghani, Rumen Iliev & Stefan Kaufmann (2012). Causal Explanation and Fact Mutability in Counterfactual Reasoning. Mind and Language 27 (1):55-85.score: 48.0
    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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  25. Mathias Frisch, Causal Reasoning in Physics.score: 48.0
    In this paper I examine several neo-Russellian arguments for the claim that there is no room for an asymmetric notion of cause in mature physical theories. I argue that these arguments are unsuccessful and discuss an example where an asymmetric causal condition plays an important role in the derivation of a physical law.
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  26. Moisés Goldszmidt & Judea Pearl (1996). Qualitative Probabilities for Default Reasoning, Belief Revision, and Causal Modeling. Artificial Intelligence 84:57-112.score: 48.0
    This paper presents a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery for deriving deductively closed beliefs and, like probability, it permits us to express if-then rules with different levels of firmness and to retract beliefs in response to changing observations. Rules are interpreted as order-of-magnitude approximations of conditional probabilities which impose constraints over the rankings of worlds. Inferences are supported by a unique priority ordering on rules (...)
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  27. 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.score: 48.0
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  28. 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.score: 48.0
    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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  29. 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.score: 48.0
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  30. Isabelle Drouet (2012). Causal Reasoning, Causal Probabilities, and Conceptions of Causation. Studies in History and Philosophy of Science Part C 43 (4):761-768.score: 45.0
  31. Michael Baumgartner & Isabelle Drouet (2013). Identifying Intervention Variables. European Journal for Philosophy of Science 3 (2):183-205.score: 45.0
    The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as so-called intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal discovery, the first part of this paper shows that the interventionist nature of variables cannot, in principle, be established based only on an interventionist notion of causation. The second part then demonstrates that standard observational methods that draw on Bayesian (...)
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  32. 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.score: 45.0
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  33. John Norton, Is There an Independent Principle of Causality in Physics? A Comment on Matthias Frisch, 'Causal Reasoning in Physics.'.score: 45.0
    Earlier version on philsci-archive.pitt.edu; latest version.
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  34. Judea Pearl (2013). Structural Counterfactuals: A Brief Introduction. Cognitive Science 37 (6):977-985.score: 45.0
    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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  35. 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.score: 45.0
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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.score: 45.0
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  37. 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.score: 45.0
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  38. 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.score: 45.0
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  39. 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.score: 45.0
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  40. 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.score: 45.0
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  41. Paul L. Harris, Tim German & Patrick Mills (1996). Children's Use of Counterfactual Thinking in Causal Reasoning. Cognition 61 (3):233-259.score: 45.0
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  42. Benjamin Kuipers & Jerome P. Kassirer (1984). Causal Reasoning in Medicine: Analysis of a Protocol. Cognitive Science 8 (4):363-385.score: 45.0
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  43. Ziva Kunda, Dale T. Miller & Theresa Claire (1990). Combining Social Concepts: The Role of Causal Reasoning. Cognitive Science 14 (4):551-577.score: 45.0
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  44. Vl Patel, Gj Groen & As Chawla (1988). Causal Reasoning About Complex Physiological-Mechanisms by Novices. Bulletin of the Psychonomic Society 26 (6):491-491.score: 45.0
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  45. 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.score: 45.0
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  46. Barbara A. Spellman & Dieynaba G. Ndiaye (2007). On the Relation Between Counterfactual and Causal Reasoning. Behavioral and Brain Sciences 30 (5-6):466-467.score: 45.0
    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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  47. 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.score: 45.0
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  48. Chen-Fang Tsai (2002). Genetic Algorithms with Temporal Causal Reasoning for AGENT-BASED Supply Chain Management. Aletheia 18 (2):63-78.score: 45.0
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  49. 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.score: 45.0
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  50. Michael Baumgartner (2008). The Causal Chain Problem. Erkenntnis 69 (2):201 - 226.score: 42.0
    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 (...)
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