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  1. François Beets (2000). Hume's Defence of Causal Inference. Dialogue 39 (2):404-406.
  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. 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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  5. 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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  6. 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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  7. 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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  8. 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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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. 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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  15. Srećko Kovač (2012). Modal Collapse in Gödel's Ontological Proof. In Miroslaw Szatkowski (ed.), Ontological Proofs Today. Ontos Verlag. 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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  16. 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.
    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 asking (...)
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  17. 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.
    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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  18. Teresa McCormack, Christoph Hoerl & Stephen Andrew Butterfill (eds.) (2011). Tool Use and Causal Cognition. OUP Oxford.
    What cognitive abilities underpin the use of tools, and how are tools and their properties represented or understood by tool-users? Does the study of tool use provide us with a unique or distinctive source of information about the causal cognition of tool-users? -/- Tool use is a topic of major interest to all those interested in animal cognition, because it implies that the animal has knowledge of the relationship between objects and their effects. There are countless examples of animals developing (...)
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  19. 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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  20. 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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  21. 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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  22. 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 primarily on learning from single trials under (...)
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  23. 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 (2013). Introduction to Special Issue on 'Actual Causation'. Erkenntnis 78 (1):1-8.
  4. J. Fetzer (ed.) (1988). Probability and Causality. D. Reidel.
  5. 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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  6. Donald Gillies & Aidan Sudbury (2013). Should Causal Models Always Be Markovian? The Case of Multi-Causal Forks in Medicine. European Journal for Philosophy of Science 3 (3):275-308.
    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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  7. 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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  8. 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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  9. 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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  10. 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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  11. Joseph Y. Halpern & Christopher Hitchcock (forthcoming). Graded Causation and Defaults. British Journal for the Philosophy of Science:axt050.
    Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality. A number of philosophers and computer scientists have also suggested that an appeal to such factors can help deal with problems facing existing accounts of actual causation. This article develops a flexible formal framework for incorporating defaults, typicality, and normality into an account of actual causation. The resulting account takes actual causation to be both graded and (...)
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  12. 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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  13. 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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  14. Christopher Hitchcock (2007). Prevention, Preemption, and the Principle of Sufficient Reason. Philosophical Review 116 (4):495-532.
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  15. Christopher Hitchcock (2001). The Intransitivity of Causation Revealed in Equations and Graphs. Journal of Philosophy 98 (6):273-299.
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  16. 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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  17. 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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  18. Christian List, Aggregating Causal Judgements.
  19. Conor Mayo-Wilson (2011). The Problem of Piecemeal Induction. Philosophy of Science 78 (5):864-874.
    It is common to assume that the problem of induction arises only because of small sample sizes or unreliable data. In this paper, I argue that the piecemeal collection of data can also lead to underdetermination of theories by evidence, even if arbitrarily large amounts of completely reliable experimental and observational data are collected. Specifically, I focus on the construction of causal theories from the results of many studies (perhaps hundreds), including randomized controlled trials and observational studies, where the studies (...)
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  20. 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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  21. 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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  22. 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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  23. Elliott Sober (2011). Reichenbach's Cubical Universe and the Problem of the External World. Synthese 181 (1):3 - 21.
    This paper is a sympathetic critique of the argument that Reichenbach develops in Chap. 2 of Experience and Prediction for the thesis that sense experience justifies belief in the existence of an external world. After discussing his attack on the positivist theory of meaning, I describe the probability ideas that Reichenbach presents. I argue that Reichenbach begins with an argument grounded in the Law of Likelihood but that he then endorses a different argument that involves prior probabilities. I try to (...)
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  24. Jan Sprenger (2011). Science Without (Parametric) Models: The Case of Bootstrap Resampling. Synthese 180 (1):65 - 76.
    Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make (...)
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  25. Chandra Sripada, Richard Gonzalez, Daniel Kessler, Eric Laber, Sara Konrath & Vijay Nair, A Reply to Rose, Livengood, Sytsma, and Machery.
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  26. 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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  27. Naftali Weinberger (2014). Evidence-Based Policy: A Practical Guide to Doing It Better, Nancy Cartwright and Jeremy Hardie. Oxford University Press, 2013, Ix + 196 Pages. [REVIEW] Economics and Philosophy 30 (1):113-120.
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