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  1. François Beets (2000). Hume's Defence of Causal Inference. Dialogue 39 (2):404-406.
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  2. 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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  3. 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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  4. 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.
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  5. Mathias Frisch, Causal Reasoning in Physics.
    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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  6. 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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  7. 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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  8. Christoph Hoerl (2011). Perception, Causal Understanding, and Locality. In Johannes Roessler, Hemdat Lerman & Naomi Eilan (eds.), Perception, Causation, and Objectivity. Oxford University Press.
    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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  9. James M. Joyce (2010). Causal Reasoning and Backtracking. Philosophical Studies 147 (1).
    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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  10. 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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  11. Srećko Kovač (2012). Modal Collapse in Gödel's Ontological Proof. In Miroslaw Szatkowski (ed.), Ontological Proofs Today. Ontos Verlag.
    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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  12. 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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  13. Dominick A. Rizzi (1994). Causal Reasoning and the Diagnostic Process. Theoretical Medicine and Bioethics 15 (3).
    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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  14. 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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  15. Richard Scheines, Teaching the Normative Theory of Causal Reasoning.
    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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  16. 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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Causal Modeling
  1. M. Albert (2007). The Propensity Theory: A Decision-Theoretic Restatement. Synthese 156 (3):587 - 603.
    Probability theory is important because of its relevance for decision making, which also means: its relevance for the single case. The propensity theory of objective probability, which addresses the single case, is subject to two problems: Humphreys’ problem of inverse probabilities and the problem of the reference class. The paper solves both problems by restating the propensity theory using (an objectivist version of) Pearl’s approach to causality and probability, and by applying a decision-theoretic perspective. Contrary to a widely held view, (...)
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  2. Michael Baumgartner (forthcoming). Detecting Causal Chains in Small-N Data. Field Methods.
    The first part of this paper shows that Qualitative Comparative Analysis (QCA)--also in its most recent forms as presented in Ragin (2000, 2008)--, does not correctly analyze data generated by causal chains, which, after all, are very common among causal processes in the social sciences. The incorrect modeling of data originating from chains essentially stems from QCA’s reliance on Quine-McCluskey optimization to eliminate redundancies from sufficient and necessary conditions. Baumgartner (2009a,b) has introduced a Boolean methodology, termed Coincidence Analysis (CNA), that (...)
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  3. Michael Baumgartner & Luke Glynn (forthcoming). Introduction to Special Issue on 'Actual Causation'. Erkenntnis:1-8.
    An actual cause of some token effect is itself a (distinct) token event (or fact, or state of affairs, …) that helped to bring about that effect. The notion of an actual cause is different from that of a potential cause – for example a pre-empted backup – which had the capacity to bring about the effect, but which wasn't in fact operative on the occasion in question. Sometimes actual causes are also distinguished from mere background conditions: as when we (...)
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  4. Philippe Gagnon (2010). L'exigence de l'Explication En Biologie au Regard d'Une Philosophie de la Morphogenèse. Eikasia. Revista de Filosofía 35 (November):123-180.
    In a first part I present the results of the philosophy of scientific explanation with an attempt to apply them to the case of the theory of evolution. Then I observe that the requirements of modelization of phenomena with the help of inductive logic do not capture efficiently the pertinent factors and fail just as much to exclude those which end up being neutral as explanatory premises. I then query in the direction of confirmation theory, and show that probabilistic reasoning (...)
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  5. Donald Gillies & Aidan Sudbury, Should Causal Models Always Be Markovian? The Case of Multi-Causal Forks in Medicine.
    The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second position by considering the multi-causal (...)
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  6. Clark Glymour & Richard Scheines (1986). Causal Modeling with the TETRAD Program. Synthese 68 (1):37 - 63.
    Drawing substantive conclusions from linear causal models that perform acceptably on statistical tests is unreasonable if it is not known how alternatives fare on these same tests. We describe a computer program, TETRAD, that helps to search rapidly for plausible alternatives to a given causal structure. The program is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence. We describe these principles, discuss how TETRAD employs them, and argue that these principles make TETRAD an effective tool. (...)
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  7. Luke Glynn (2011). A Probabilistic Analysis of Causation. British Journal for the Philosophy of Science 62 (2):343-392.
    The starting point in the development of probabilistic analyses of token causation has usually been the naïve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation by absence and omission. I show that (...)
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  8. Moisés Goldszmidt & Judea Pearl (1996). Qualitative Probabilities for Default Reasoning, Belief Revision, and Causal Modeling. Artificial Intelligence 84:57-112.
    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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  9. N. Hall (2007). Structural Equations and Causation. Philosophical Studies 132 (1):109 - 136.
    Structural equations have become increasingly popular in recent years as tools for understanding causation. But standard structural equations approaches to causation face deep problems. The most philosophically interesting of these consists in their failure to incorporate a distinction between default states of an object or system, and deviations therefrom. Exploring this problem, and how to fix it, helps to illuminate the central role this distinction plays in our causal thinking.
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  10. Joseph Halpern & Christopher Hitchcock, Graded Causation and Defaults.
    This paper extends the account of actual causation offered by Halpern and Pearl [2005]. We show that this account yields the wrong judgment in certain classes of cases. We offer a revised definition that incorporates consideration of defaults, typicality, and normality. The revised definition takes actual causation to be both graded and comparative. We then apply our definition to a number of cases.
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  11. Toby Handfield, Charles R. Twardy, Kevin B. Korb & Graham Oppy (2008). The Metaphysics of Causal Models: Where's the Biff? Erkenntnis 68 (2):149-68.
    This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases of late preëmption and other cases (...)
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  12. Christopher Hitchcock (2009). Structural Equations and Causation: Six Counterexamples. Philosophical Studies 144 (3):391 - 401.
    Hall [(2007), Philosophical Studies, 132, 109–136] offers a critique of structural equations accounts of actual causation, and then offers a new theory of his own. In this paper, I respond to Hall’s critique, and present some counterexamples to his new theory. These counterexamples are then diagnosed.
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  13. Christopher Hitchcock (2007). Prevention, Preemption, and the Principle of Sufficient Reason. Philosophical Review 116 (4):495-532.
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  14. Christopher Hitchcock (2001). The Intransitivity of Causation Revealed in Equations and Graphs. Journal of Philosophy 98 (6):273-299.
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  15. Gürol Irzik (1996). Can Causes Be Reduced to Correlations? British Journal for the Philosophy of Science 47 (2):249-270.
    This paper argues against Papineau's claim that causal relations can be reduced to correlations and defends Cartwright's thesis that they can be nevertheless boot-strapped from them, given sufficiently rich causal background knowledge.
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  16. Gurol Irzik & Eric Meyer (1987). Causal Modeling: New Directions for Statistical Explanation. Philosophy of Science 54 (4):495-514.
    Causal modeling methods such as path analysis, used in the social and natural sciences, are also highly relevant to philosophical problems of probabilistic causation and statistical explanation. We show how these methods can be effectively used (1) to improve and extend Salmon's S-R basis for statistical explanation, and (2) to repair Cartwright's resolution of Simpson's paradox, clarifying the relationship between statistical and causal claims.
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  17. Osvaldo Pessoa Jr (2010). Computation of Probabilities in Causal Models of History of Science. Principia 10 (2):109-124.
    The aim of this paper is to investigate the ascription of probabilities in a causal model of an episode in the history of science. The aim of such a quantitative approach is to allow the implementation of the causal model in a computer, to run simulations. As an example, we look at the beginning of the science of magnetism, “explaining” — in a probabilistic way, in terms of a single causal model — why the field advanced in China but not (...)
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  18. Federica Russo (2006). The Rationale of Variation in Methodological and Evidential Pluralism. Philosophica 77.
    Causal analysis in the social sciences takes advantage of a variety of methods and of a multi-fold source of information and evidence. This pluralistic methodology and source of information raises the question of whether we should accordingly have a pluralistic metaphysics and epistemology. This paper focuses on epistemology and argues that a pluralistic methodology and evidence don’t entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, based on the notion of variation. Further, (...)
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  19. Steven A. Sloman, Philip M. Fernbach & Scott Ewing (2012). A Causal Model of Intentionality Judgment. Mind and Language 27 (2):154-180.
    We propose a causal model theory to explain asymmetries in judgments of the intentionality of a foreseen side-effect that is either negative or positive (Knobe, 2003). The theory is implemented as a Bayesian network relating types of mental states, actions, and consequences that integrates previous hypotheses. It appeals to two inferential routes to judgment about the intentionality of someone else's action: bottom-up from action to desire and top-down from character and disposition. Support for the theory comes from three experiments that (...)
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  20. Jan Sprenger (forthcoming). Science Without (Parametric) Models: The Case of Bootstrap Resampling. Synthese.
    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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  21. Chandra Sripada, Richard Gonzalez, Daniel Kessler, Eric Laber, Sara Konrath & Vijay Nair, A Reply to Rose, Livengood, Sytsma, and Machery.
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  22. Chandra Sripada & Sara Konrath (2011). Telling More Than We Can Know About Intentional Action. Mind and Language 26 (3):353-380.
    Recently, a number of philosophers have advanced a surprising conclusion: people's judgments about whether an agent brought about an outcome intentionally are pervasively influenced by normative considerations. In this paper, we investigate the ‘Chairman case’, an influential case from this literature and disagree with this conclusion. Using a statistical method called structural path modeling, we show that people's attributions of intentional action to an agent are driven not by normative assessments, but rather by attributions of underlying values and characterological dispositions (...)
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  23. Brad Weslake, A Partial Theory of Actual Causation.
    One part of the true theory of actual causation is a set of conditions responsible for eliminating all of the non-causes of an effect that can be discerned at the level of counterfactual structure. I defend a proposal for this part of the theory.
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  24. Gregory Wheeler & Richard Scheines (forthcoming). Coherence and Confirmation Through Causation. Mind.
    Coherentism maintains that coherent beliefs are more likely to be true than incoherent beliefs, and that coherent evidence provides more confirmation of a hypothesis when the evidence is made coherent by the explanation provided by that hypothesis. Although probabilistic models of credence ought to be well-suited to justifying such claims, negative results from Bayesian epistemology have suggested otherwise. In this essay we argue that the connection between coherence and confirmation should be understood as a relation mediated by the causal relationships (...)
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Causal Reasoning, Misc
  1. Mark Alicke, David Rose & Dori Bloom (2011). Causation, Norm Violation, and Culpable Control. Journal of Philosophy 108 (12):670-696.
  2. Michael Baumgartner (forthcoming). Detecting Causal Chains in Small-N Data. Field Methods.
    The first part of this paper shows that Qualitative Comparative Analysis (QCA)--also in its most recent forms as presented in Ragin (2000, 2008)--, does not correctly analyze data generated by causal chains, which, after all, are very common among causal processes in the social sciences. The incorrect modeling of data originating from chains essentially stems from QCA’s reliance on Quine-McCluskey optimization to eliminate redundancies from sufficient and necessary conditions. Baumgartner (2009a,b) has introduced a Boolean methodology, termed Coincidence Analysis (CNA), that (...)
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  3. Michael Baumgartner (2008). The Causal Chain Problem. 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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  4. Martijn Blaauw (ed.) (forthcoming). Contrastivism in Philosophy. Routledge.
  5. Isabelle Drouet (2012). Causal Reasoning, Causal Probabilities, and Conceptions of Causation. Studies in History and Philosophy of Science Part C 43 (4):761-768.
  6. Ahmet Ekici & Sule Onsel (forthcoming). How Ethical Behavior of Firms is Influenced by the Legal and Political Environments: A Bayesian Causal Map Analysis Based on Stages of Development. Journal of Business Ethics.
  7. Clark Glymour, Causal Mechanism and Probability: A Normative Approach.
    & Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic (...)
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  8. Alison Gopnik & Laura Schulz (eds.) (2007). Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
    Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and (...)
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  9. Christopher Hitchcock (2003). Of Humean Bondage. British Journal for the Philosophy of Science 54 (1):1-25.
    There are many ways of attaching two objects together: for example, they can be connected, linked, tied or bound together; and the connection, link, tie or bind can be made of chain, rope, or cement. Every one of these binding methods has been used as a metaphor for causation. What is the real significance of these metaphors? They express a commitment to a certain way of thinking about causation, summarized in the following thesis: ‘In any concrete situation, there is an (...)
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  10. Joshua Knobe (2010). Person as Scientist, Person as Moralist. Behavioral and Brain Sciences 33:315-329.
    It has often been suggested that people’s ordinary capacities for understanding the world make use of much the same methods one might find in a formal scientific investigation. A series of recent experimental results offer a challenge to this widely-held view, suggesting that people’s moral judgments can actually influence the intuitions they hold both in folk psychology and in causal cognition. The present target article distinguishes two basic approaches to explaining such effects. One approach would be to say that the (...)
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  11. Christopher Mole (2012). Three Philosophical Lessons for the Analysis of Criminal and Military Intelligence. Intelligence and National Security 27 (4):441-58.
    It has recently been suggested that philosophy – in particular epistemology – has a contribution to make to the analysis of criminal and military intelligence. The present article pursues this suggestion, taking three phenomena that have recently been studied by philosophers, and showing that they have important implications for the gathering and sharing of intelligence, and for the use of intelligence in the determining of military strategy. The phenomena discussed are: (1) Simpson's Paradox, (2) the distinction between resiliency and reliability (...)
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  12. Lennart Nordenfelt & B. I. B. Lindahl (eds.) (1984). Health, Disease, and Causal Explanations in Medicine. Reidel.
  13. Craig Roxborough & Jill Cumby (2009). Folk Psychological Concepts: Causation. Philosophical Psychology 22 (2):205-213.
    Which factors influence the folk application of the concept of causation? Knobe has argued that causal judgments are primarily influenced by the moral valence of the behavior under consideration. Whereas Driver has pointed out that the data Knobe relies on can also be used to support the claim that it is the atypicality of the agent's behavior that influences our willingness to assign causality to that agent. While Knobe and Fraser have provided a further study to address the cogency of (...)
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  14. David H. Sanford (2009). Causation. In Jaegwon Kim, Ernest Sosa & Gary S. Rosenkrantz (eds.), A Companion to Metaphysics. Wiley-Blackwell.
    Twenty-one paragraphs in this entry begin with a statement of a view about causation. To help organize the entry, the next sentence then classifies the view as 'prevailing, majority, controversial', or 'minority'. The following brief discussions attempt to be clear and fair. Respect for fairness, however, does not prevent the author from referring to his own views. For example, the author classifies "There is no element of genuine a priori reasoning in causal inference" as a majority view. After expounded the (...)
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  15. David H. Sanford (1994). Causation and Intelligibility. Philosophy 69 (267):55-.
    Hume, in "An Enquiry Concerning Human Understanding", holds (1) that all causal reasoning is based on experience and (2) that causal reasoning is based on nothing but experience. (1) does not imply (2), and Hume's good reasons for (1) are not good reasons for (2). This essay accepts (1) and argues against (2). A priori reasoning plays a role in causal inference. Familiar examples from Hume and from classroom examples of sudden disappearances and radical changes do not show otherwise. A (...)
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  16. Jonathan Schaffer (2012). Causal Contextualisms. In Martijn Blaauw (ed.), Contrastivism in Philosophy: New Perspectives. Routledge.
    Causal claims are context sensitive. According to the old orthodoxy (Mackie 1974, Lewis 1986, inter alia), the context sensitivity of causal claims is all due to conversational pragmatics. According to the new contextualists (Hitchcock 1996, Woodward 2003, Maslen 2004, Menzies 2004, Schaffer 2005, and Hall ms), at least some of the context sensitivity of causal claims is semantic in nature. I want to discuss the prospects for causal contextualism, by asking why causal claims are context sensitive, what they are sensitive (...)
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  17. Michael Strevens (2007). Why Represent Causal Relations? In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, Computation. Oxford University Press.
    Why do we represent the world around us using causal generalizations, rather than, say, purely statistical generalizations? Do causal representations contain useful additional information, or are they merely more efficient for inferential purposes? This paper considers the second kind of answer: it investigates some ways in which causal cognition might aid us not because of its expressive power, but because of its organizational power. Three styles of explanation are considered. The first, building on the work of Reichenbach in "The Direction (...)
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  18. Justin Sytsma, Jonathan Livengood & David Rose (2012). Two Types of Typicality: Rethinking the Role of Statistical Typicality in Ordinary Causal Attributions. Studies in History and Philosophy of Science Part C 43 (4):814-820.
    Empirical work on the use of causal language by ordinary people indicates that their causal attributions tend to be sensitive not only to purely descriptive considerations, but also to broadly moral considerations. For example, ordinary causal attributions appear to be highly sensitive to whether a behavior is permissible or impermissible. Recently, however, a consensus view has emerged that situates the role of permissibility information within a broader framework: According to the consensus, ordinary causal attributions are sensitive to whether or not (...)
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