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  1. Keith Augustine & Yonatan I. Fishman (2015). The Dualist’s Dilemma: The High Cost of Reconciling Neuroscience with a Soul. In Keith Augustine & Michael Martin (eds.), The Myth of an Afterlife: The Case against Life After Death. Rowman & Littlefield. pp. 203-292.
    Tight correlations between mental states and brain states have been observed time and again within the ethology of biologically ingrained animal behaviors, the comparative psychology of animal minds, the evolutionary psychology of mental adaptations, the behavioral genetics of inherited mental traits, the developmental psychology of the maturing mind, the psychopharmacology of mind-altering substances, and cognitive neuroscience more generally. They imply that our mental lives are only made possible because of brain activity—that having a functioning brain is a necessary condition for (...)
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  2. Julia R. Badger & Laura R. Shapiro (2014). Category Structure Affects the Developmental Trajectory of Children's Inductive Inferences for Both Natural Kinds and Artefacts. Thinking and Reasoning 21 (2):206-229.
    Inductive reasoning is fundamental to human cognition, yet it remains unclear how we develop this ability and what might influence our inductive choices. We created novel categories in which crucial factors such as domain and category structure were manipulated orthogonally. We trained 403 4–9-year-old children to categorise well-matched natural kind and artefact stimuli with either featural or relational category structure, followed by induction tasks. This wide age range allowed for the first full exploration of the developmental trajectory of inductive reasoning (...)
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  3. Aron Barbey & Phillip Wolff, Learning Causal Structure From Reasoning.
    According to the transitive dynamics model, people can construct causal structures by linking together configurations of force. The predictions of the model were tested in two experiments in which participants generated new causal relationships by chaining together two (Experiment 1) or three (Experiment 2) causal relations. The predictions of the transitive dynamics model were compared against those of Goldvarg and Johnson-Laird’s model theory (Goldvarg & Johnson- Laird, 2001). The transitive dynamics model consistently predicted the overall causal relationship drawn by participants (...)
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  4. François Beets (2000). Hume's Defence of Causal Inference. [REVIEW] Dialogue 39 (2):404-406.
  5. 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 Tollens, Denial of the Antecedent, and Affirmation of (...)
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  6. 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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  7. Al Brown (1986). A Search for Causal Mechanisms Guides Learning in Novel Domains. Bulletin of the Psychonomic Society 24 (5):334-334.
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  8. Christopher D. Carroll & Patricia W. Cheng (2010). The Induction of Hidden Causes: Causal Mediation and Violations of Independent Causal Influence. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 913--918.
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  9. Ma Casteel (1992). Childrens Inferences About Ambiguous Events-the Effects of Foregrounding and Multiple Clues. Bulletin of the Psychonomic Society 30 (6):477-477.
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  10. Daniele Chiffi (2013). Causal Attribution and Crossing Over Between Probabilities in Clinical Diagnosis. In Christer Svennerlind, Jan Almäng & Rögnvaldur Ingthorsson (eds.), Johanssonian Investigations. Ontos Verlag. pp. 5--191.
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  11. François Claveau (2011). Evidential Variety as a Source of Credibility for Causal Inference: Beyond Sharp Designs and Structural Models. Journal of Economic Methodology 18 (3):233-253.
    There is an ongoing debate in economics between the design-based approach and the structural approach. The main locus of contention regards how best to pursue the quest for credible causal inference. Each approach emphasizes one element ? sharp study designs versus structural models ? but these elements have well-known limitations. This paper investigates where a researcher might look for credibility when, for the causal question under study, these limitations are binding. It argues that seeking variety of evidence ? understood specifically (...)
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  12. David Danks (2005). The Supposed Competition Between Theories of Human Causal Inference. 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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  13. David Joseph Danks (2001). The Epistemology of Causal Judgment. Dissertation, University of California, San Diego
    We make constant use of causal beliefs in our everyday lives without giving much thought to the source of those beliefs, even for situations about which we have no specific prior causal knowledge. We can ask two distinct types of questions about these causal judgments: descriptive questions and normative questions . The primary goal of this dissertation is to apply normative research on causal judgment to our descriptive theories. ;I begin this dissertation by describing the primary results of research on (...)
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  14. Leen De Vreese & Erik Weber (2004). Applications of the Adaptive Logic for Causal Discovery. Logique Et Analyse 185 (188):33-51.
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  15. Benoit Desjardins (1999). On the Theoretical Limits to Reliable Causal Inference. Dissertation, University of Pittsburgh
    One of the most central problems in scientific research is the search for explanations of some aspect of nature for which empirical data is available. One seeks to identify the causal processes explaining the data, in the form of a model of the aspect of nature under study. Although traditional statistical approaches are excellent for finding statistical dependencies in a body of empirical data, they prove inadequate at finding the causal structure in the data. New graphical algorithmic approaches have been (...)
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  16. Stefan Dragulinescu (2012). On 'Stabilising' Medical Mechanisms, Truth-Makers and Epistemic Causality: A Critique to Williamson and Russo's Approach. Synthese 187 (2):785-800.
    In this paper I offer an anti-Humean critique to Williamson and Russo’s approach to medical mechanisms. I focus on one of the specific claims made by Williamson and Russo, namely the claim that micro-structural ‘mechanisms’ provide evidence for the stability across populations of causal relationships ascertained at the (macro-) level of (test) populations. This claim is grounded in the epistemic account of causality developed by Williamson, an account which—while not relying exclusively on mechanistic evidence for justifying causal judgements—appeals nevertheless to (...)
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  17. 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. Oxford University Press.
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  18. Frederick Eberhardt (2013). Direct Causes and the Trouble with Soft Interventions. Erkenntnis 79 (4):1-23.
    An interventionist account of causation characterizes causal relations in terms of changes resulting from particular interventions. I provide a new example of a causal relation for which there does not exist an intervention satisfying the common interventionist standard. I consider adaptations that would save this standard and describe their implications for an interventionist account of causation. No adaptation preserves all the aspects that make the interventionist account appealing. Part of the fallout is a clearer account of the difficulties in characterizing (...)
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  19. Frederick Eberhardt (2009). Introduction to the Epistemology of Causation. 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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  20. 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 critique of anti-causal arguments and partly (...)
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  21. 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. C. Glymour, P. Spirtes & R. Scheines (1991). Causal Inference. Erkenntnis 35 (1-3):151 - 189.
    We have examined only a few of the basic questions about causal inference that result from Reichenbach's two principles. We have not considered what happens when the probability distribution is a mixture of distributions from different causal structures, or how unmeasured common causes can be detected, or what inferences can reliably be drawn about causal relations among unmeasured variables, or the exact advantages that experimental control offers. A good deal is known about these questions, and there is a good deal (...)
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  23. Clark Glymour (1998). Learning Causes: Psychological Explanations of Causal Explanation. [REVIEW] Minds and Machines 8 (1):39-60.
    I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, (...)
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  24. Clark Glymour, Alison Gopnik, David M. Sobel & Laura E. Schulz, Causal Learning Mechanisms in Very Young Children: Two-, Three-, and Four-Year-Olds Infer Causal Relations From Patterns of Variation and Covariation.
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  25. Clark Glymour, Richard Scheines & Peter Spirtes, Exploring Causal Structure with the TETRAD Program.
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  26. Clark Glymour, Richard Scheines, Peter Spirtes & Kevin T. Kelly, Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
    Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
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  27. Alison Gopnik, Young Children Infer Causal Strength From Probabilities and Interventions.
    Word count (excluding abstract and references): 2,498 words. Address for correspondence: T. Kushnir, Psychology Department, University of California, 3210 Tolman Hall #1650, Berkeley, CA 94720-1650. Phone: 510-205-9847. Fax: 510-642- 5293. E-mail: tkushnir@berkeley.edu.
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  28. Rolf Haenni & Stephan Hartmann (2006). Causality, Uncertainty and Ignorance. Minds and Machines 16 (3).
    Special issue. With contributions by Malcolm Forster, Rocio Garcia-Rotamero and Ulrich Hoffrage, Christian Jakob, Kevin Korb and Erik Nyberg, Michael Smithson, Daniel Steel, Brad Weslake, and Jon Williamson.
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  29. 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 condition, which informally states that variables are (...)
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  30. Yongqun He, Sirarat Sarntivijai, Yu Lin, Zuoshuang Xiang, Abra Guo, Shelley Zhang, Desikan Jagannathan, Luca Toldo, Cui Tao & Barry Smith (2014). OAE: The Ontology of Adverse Events. Journal of Biomedical Semantics 5 (29).
    A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description: The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data (...)
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  31. Michael Heidelberger (2011). Causal and Symbolic Understanding in Historical Epistemology. Erkenntnis 75 (3):467-482.
    The term “historical epistemology” can be read in two different ways: (1) as referring to a program of ‘historicizing’ epistemology, in the sense of a critique of traditional epistemology’s tendency to gloss over historical context, or (2) as a manifesto of ‘epistemologizing’ history, i.e. as a critique of radical historicist and relativist approaches. In this paper I will defend a position in this second sense. I show that one can account for the historical development and diversity of science without disavowing (...)
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  32. 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, respectively. I (...)
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  33. Christoph Hoerl (2011). Perception, Causal Understanding, and Locality. In Johannes Roessler, Hemdat Lerman & Naomi Eilan (eds.), Perception, Causation, and Objectivity. Oxford University Press. pp. 207.
    Contemporary philosophical debates about causation are dominated by two approaches, which are often referred to as difference-making and causal process approaches to causation, respectively. I provide a characterization of the dialectic between these two approaches, on which that dialectic turns crucially on the question as to whether our common sense concept of causation involves a commitment to locality – i.e., to the claim that causal relations are always subject to spatial constraints. I then argue that we can extract from existing (...)
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  34. Phyllis McKay Illari (2011). Mechanistic Evidence: Disambiguating the Russo–Williamson Thesis. International Studies in the Philosophy of Science 25 (2):139 - 157.
    Russo and Williamson claim that establishing causal claims requires mechanistic and difference-making evidence. In this article, I will argue that Russo and Williamson's formulation of their thesis is multiply ambiguous. I will make three distinctions: mechanistic evidence as type vs object of evidence; what mechanism or mechanisms we want evidence of; and how much evidence of a mechanism we require. I will feed these more precise meanings back into the Russo?Williamson thesis and argue that it is both true and false: (...)
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  35. 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 component (...)
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  36. Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
    Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...)
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  37. Joshua Knobe & Ben Fraser (2008). Causal Judgment and Moral Judgment: Two Experiments. In Walter Sinnott-Armstrong (ed.), Moral Psychology. MIT Press.
    It has long been known that people’s causal judgments can have an impact on their moral judgments. To take a simple example, if people conclude that a behavior caused the death of ten innocent children, they will therefore be inclined to regard the behavior itself as morally wrong. So far, none of this should come as any surprise. But recent experimental work points to the existence of a second, and more surprising, aspect of the relationship between causal judgment and moral (...)
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  38. Jonathan F. Kominsky, Jonathan Phillips, Tobias Gerstenberg, David Lagnado & Joshua Knobe (2015). Causal Superseding. Cognition 137:196-209.
    When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as "causal superseding." We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that this causal (...)
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  39. Srećko Kovač (2012). Modal Collapse in Gödel's Ontological Proof. In Miroslaw Szatkowski (ed.), Ontological Proofs Today. Ontos Verlag. pp. 50--323.
    After introductory reminder of and comments on Gödel’s ontological proof, we discuss the collapse of modalities, which is provable in Gödel’s ontological system GO. We argue that Gödel’s texts confirm modal collapse as intended consequence of his ontological system. Further, we aim to show that modal collapse properly fits into Gödel’s philosophical views, especially into his ontology of separation and union of force and fact, as well as into his cosmological theory of the nonobjectivity of the lapse of time. As (...)
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  40. David R. Mandel (2005). Counterfactual and Causal Explanation: From Early Theoretical Views to New Frontiers. In David R. Mandel, Denis J. Hilton & Patrizia Catellani (eds.), The Psychology of Counterfactual Thinking. Routledge.
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  41. Teresa McCormack & Christoph Hoerl (2011). Tool Use, Planning and Future Thinking in Children and Animals. In Teresa McCormack, Christoph Hoerl & Stephen Butterfill (eds.), Tool use and causal cognition. Oxford University Press. pp. 129.
    This chapter considers in what sense, if any, planning and future thinking is involved both in the sort of behaviour examined by McCarty et al. (1999) and in the sort of behaviour measured by researchers creating versions of Tulving's spoon test. It argues that mature human planning and future thinking involves a particular type of temporal cognition, and that there are reasons to be doubtful as to whether either of those two approaches actually assesses this type of cognition. To anticipate, (...)
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  42. Graham Moran (1979). Knowledge of Causes.
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  43. 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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  44. 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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  45. 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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  46. Federica Russo, Causality and Causal Modelling in the Social Sciences.
    The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...)
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  47. Iñaki San Pedro (2011). Venetian Sea Levels, British Bread Prices and the Principle of the Common Cause: A Reassessment. 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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  48. 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. pp. 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 primarily on learning from single trials under (...)
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  49. Walter Sinnott-Armstrong (ed.) (2008). Moral Psychology, 3 Vols. MIT Press.
    For much of the twentieth century, philosophy and science went their separate ways. In moral philosophy, fear of the so-called naturalistic fallacy kept moral philosophers from incorporating developments in biology and psychology. Since the 1990s, however, many philosophers have drawn on recent advances in cognitive psychology, brain science, and evolutionary psychology to inform their work. This collaborative trend is especially strong in moral philosophy, and these three volumes bring together some of the most innovative work by both philosophers and psychologists (...)
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  50. Peter Spirtes & Richard Scheines, Causal Inference and Ambiguous Manipulations.
    Peter Spirtes and Richard Scheines. Causal Inference and Ambiguous Manipulations.
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