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  1. Daniel Steel, Extrapolation, Capacities, and Mechanisms.
    (Chapter 5 of Across the Boundaries, forthcoming, from Oxford University Press) This chapter argues that previous accounts of extrapolation, either by reference to capacities or mechanisms, do not adequately address the challenges confronting extrapolation. It then begins the account of how the mechanisms-approach can be developed so as to do better. The central concept in this account is what I term comparative process tracing.
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  2. Daniel Steel, Inductive Rules Are No Problem.
    This essay defends the view that inductive reasoning involves following inductive rules against objections that inductive rules are undesirable because they ignore background knowledge and unnecessary because Bayesianism is not an inductive rule. I propose that inductive rules be understood as sets of functions from data to hypotheses that are intended as solutions to inductive problems. According to this proposal, background knowledge is important in the application of inductive rules and Bayesianism qualifies as an inductive rule. Finally, I consider a (...)
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  3. Daniel Steel, Mind Changes and Testability: How Formal and Statistical Learning Theory Converge in the New Riddle of Induction.
    This essay demonstrates a previously unnoticed connection between formal and statistical learning theory with regard to Nelson Goodman’s new riddle of induction. Discussions of Goodman’s riddle in formal learning theory explain how conjecturing “all green” before “all grue” can enhance efficient convergence to the truth, where efficiency is understood in terms of minimizing the maximum number of retractions or “mind changes.” Vapnik-Chervonenkis (VC) dimension is a central concept in statistical learning theory and is similar to Popper’s notion of degrees of (...)
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  4. Daniel Steel, Rethinking the Interpretivism Versus Naturalism Debate in the Philosophy of Social Science.
    The naturalism versus interpretivism debate in social science is traditionally framed as the question of whether social science should attempt to emulate the methods of natural science. I argue that this manner of formulating the issue is problematic insofar as it presupposes an implausibly strong unity of method among the natural sciences. I propose instead that the core question of the debate is the extent to which reliable causal inference is possible in social science, a question that cannot be answered (...)
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  5. Daniel Steel, What If the Principle of Induction is Normative? Means-Ends Epistemology and Hume's Problem.
    I develop a critique of Hume’s infamous problem of induction based upon the idea that the principle of induction (PI) is a normative rather than descriptive claim. I argue that Hume’s problem is a false dilemma, since the PI might be neither a “relation of ideas” nor a “matter of fact” but rather what I call a contingent normative statement. In this case, the PI could be justified by a means-ends argument in which the link between means and end is (...)
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  6. Daniel Steel & Megan Delehanty, Models and Mechanisms: On the Methodology of Animal Extrapolation.
    Any account of extrapolation from animal models to humans must confront two basic challenges: explain how extrapolation can be justified even when there are causally relevant differences between model and target, and explain how the suitability of a model can be established given only limited information about the target. We argue that existing approaches to extrapolation—either in terms of capacities or mechanisms—do not adequately address these challenges. However, we propose a further elaboration of the mechanisms approach that provides a better (...)
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  7. Daniel Steel & S. Kedzie Hall, Inductive Rules, Background Knowledge, and Skepticism.
    This essay defends the view that inductive reasoning involves following inductive rules against objections that inductive rules are undesirable because they ignore background knowledge and unnecessary because Bayesianism is not an inductive rule. I propose that inductive rules be understood as sets of functions from data to hypotheses that are intended as solutions to inductive problems. According to this proposal, background knowledge is important in the application of inductive rules and Bayesianism qualifies as an inductive rule. Finally, I consider a (...)
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  8. Daniel Steel (forthcoming). Acceptance, Values, and Inductive Risk. 80 (5):818-828.
    The argument from inductive risk attempts to show that practical and ethical costs of errors should influence standards of evidence for accepting scientific claims. A common objection charges that this argument presupposes a behavioral theory of acceptance that is inappropriate for science. I respond by showing that the argument from inductive risk is supported by a nonbehavioral theory of acceptance developed by Cohen, which defines acceptance in terms of premising. Moreover, I argue that theories designed to explain how acceptance can (...)
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  9. Daniel Steel (2013). Precaution and Proportionality: A Reply to Turner. Ethics, Policy and Environment 16 (3):344-348.
    (2013). Precaution and Proportionality: A Reply to Turner. Ethics, Policy & Environment. ???aop.label???. doi: 10.1080/21550085.2013.844572.
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  10. Daniel Steel (2013). The Precautionary Principle and the Dilemma Objection. Ethics, Policy and Environment 16 (3):321-340.
    The dilemma objection charges that ?weak? versions of the precautionary principle (PP) are vacuous while ?strong? ones are incoherent. I respond that the ?weak? versus ?strong? distinction is misleading and should be replaced with a contrast between PP as a meta-rule and PP proper. Meta versions of PP require that the decision-making procedures used for environmental policy not be susceptible to paralysis by scientific uncertainty. Such claims are substantive because they often recommend against basing environmental policy decisions on cost?benefit analysis. (...)
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  11. Daniel Steel & Kyle Powys Whyte (2012). Environmental Justice, Values, and Scientific Expertise. Kennedy Institute of Ethics Journal 22 (2):163-182.
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  12. Daniel Steel (2011). Causality, Causal Models, and Social Mechanisms. In Ian Jarvie Jesus Zamora Bonilla (ed.), The Sage Handbook of the Philosophy of Social Sciences. 288.
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  13. Daniel Steel (2011). Causal Inference and Medical Experiments. In Fred Gifford (ed.), Philosophy of Medicine. Elsevier. 16--159.
  14. Daniel Steel (2011). Extrapolation, Uncertainty Factors, and the Precautionary Principle. Studies in History and Philosophy of Science Part C 42 (3):356-364.
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  15. Daniel Steel (2011). On Not Changing the Problem: A Reply to Howson. International Studies in the Philosophy of Science 25 (3):285 - 291.
    Howson's critique of my essay on Hume's problem of induction levels two main charges. First, Howson claims that I have attributed to him an error that he never made, and in fact which he warned against in the very text that I cite. Secondly, Howson argues that my proposed solution to Hume's problem is flawed on technical and philosophical grounds. In response to the first charge, I explain how Howson's text justifies attributing to him the claim that the principle of (...)
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  16. Daniel Steel & S. Kedzie Hall (2011). What If the Principle of Induction Is Normative? Formal Learning Theory and Hume's Problem. International Studies in the Philosophy of Science 24 (2):171-185.
    This article argues that a successful answer to Hume's problem of induction can be developed from a sub-genre of philosophy of science known as formal learning theory. One of the central concepts of formal learning theory is logical reliability: roughly, a method is logically reliable when it is assured of eventually settling on the truth for every sequence of data that is possible given what we know. I show that the principle of induction (PI) is necessary and sufficient for logical (...)
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  17. Daniel Steel (2010). Cartwright on Causality: Methods, Metaphysics and Modularity. Economics and Philosophy 26 (1):77-86.
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  18. Daniel Steel (2010). Cartwright on Causality: Methods, Metaphysics and Modularity Hunting Causes and Using Them: Approaches in Philosophy and Economics , Nancy Cartwright. Cambridge University Press, 2008, X + 270 Pages. [REVIEW] Economics and Philosophy 26 (1):77-86.
  19. Daniel Steel (2010). Epistemic Values and the Argument From Inductive Risk. Philosophy of Science 77 (1):14-34.
    Critics of the ideal of value‐free science often assume that they must reject the distinction between epistemic and nonepistemic values. I argue that this assumption is mistaken and that the distinction can be used to clarify and defend the argument from inductive risk, which challenges the value‐free ideal. I develop the idea that the characteristic feature of epistemic values is that they promote, either intrinsically or extrinsically, the attainment of truths. This proposal is shown to answer common objections to the (...)
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  20. Daniel Steel (2010). Review of Sandra D. Mitchell, Unsimple Truths: Science, Complexity, and Policy. [REVIEW] Notre Dame Philosophical Reviews 2010 (5).
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  21. Daniel Steel & Francesco Guala (eds.) (2010). The Philosophy of Social Science Reader. Routledge.
    The Philosophy of Social Science Reader is an outstanding, comprehensive and up-to-date collection of key readings in the philosophy of social science, covering the essential issues, problems and debates in this important interdisciplinary area. Each section is carefully introduced by the editors, and the readings placed in context. The anthology is organized into seven clear parts: Values and Social Science Causal Inference and Explanation Interpretation Rationality and Choice Individualism Norms Cultural Evolution. Featuring the work of influential philosophers and social scientists (...)
     
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  22. Daniel Steel & S. Kedzie Hall (2010). A New Approach to Argument by Analogy: Extrapolation and Chain Graphs. Philosophy of Science 77 (5):1058-1069.
    In order to make scientific results relevant to practical decision making, it is often necessary to transfer a result obtained in one set of circumstances—an animal model, a computer simulation, an economic experiment—to another that may differ in relevant respects—for example, to humans, the global climate, or an auction. Such inferences, which we can call extrapolations, are a type of argument by analogy. This essay sketches a new approach to analogical inference that utilizes chain graphs, which resemble directed acyclic graphs (...)
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  23. Daniel Steel & S. Kedzie Hall (2010). Naturalism and the Enlightenment Ideal : Rethinking a Central Debate in the Philosophy of Social Science. In P. D. Magnus & Jacob Busch (eds.), New Waves in Philosophy of Science. Palgrave Macmillan.
    The naturalism versus interpretivism debate the in philosophy of social science is traditionally framed as the question of whether social science should attempt to emulate the methods of natural science. I show that this manner of formulating the issue is problematic insofar as it presupposes an implausibly strong unity of method among the natural sciences. I propose instead that what is at stake in this debate is the feasibility and desirability of what I call the Enlightenment ideal of social science. (...)
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  24. Daniel Steel (2009). Testability and Ockham's Razor: How Formal and Statistical Learning Theory Converge in the New Riddle of Induction. [REVIEW] Journal of Philosophical Logic 38 (5):471 - 489.
    Nelson Goodman’s new riddle of induction forcefully illustrates a challenge that must be confronted by any adequate theory of inductive inference: provide some basis for choosing among alternative hypotheses that fit past data but make divergent predictions. One response to this challenge is to distinguish among alternatives by means of some epistemically significant characteristic beyond fit with the data. Statistical learning theory takes this approach by showing how a concept similar to Popper’s notion of degrees of testability is linked to (...)
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  25. Daniel Steel (2008). Across the Boundaries: Extrapolation in Biology and Social Science. Oxford University Press.
    Inferences like these are known as extrapolations.
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  26. Daniel Steel (2007). Bayesian Confirmation Theory and the Likelihood Principle. Synthese 156 (1):53 - 77.
    The likelihood principle (LP) is a core issue in disagreements between Bayesian and frequentist statistical theories. Yet statements of the LP are often ambiguous, while arguments for why a Bayesian must accept it rely upon unexamined implicit premises. I distinguish two propositions associated with the LP, which I label LP1 and LP2. I maintain that there is a compelling Bayesian argument for LP1, based upon strict conditionalization, standard Bayesian decision theory, and a proposition I call the practical relevance principle. In (...)
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  27. Daniel Steel (2007). With or Without Mechanisms A Reply to Weber. Philosophy of the Social Sciences 37 (3):360-365.
    This reply to Erik Weber's commentary agrees that mechanisms are important for causal inference in social science, but argues that Weber makes the mistake that was the main focus of my original essay: inferring that since a problem cannot be solved without mechanisms, it can be solved with them. As it stands, this inference is invalid since the problem might be unsolvable with or without mechanisms. Any claim about the usefulness of mechanisms for some purpose requires an adequate account of (...)
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  28. Daniel Steel (2006). Comment on Hausman & Woodward on the Causal Markov Condition. British Journal for the Philosophy of Science 57 (1):219-231.
    Woodward present an argument for the Causal Markov Condition (CMC) on the basis of a principle they dub ‘modularity’ ([1999, 2004]). I show that the conclusion of their argument is not in fact the CMC but a substantially weaker proposition. In addition, I show that their argument is invalid and trace this invalidity to two features of modularity, namely, that it is stated in terms of pairwise independence and ‘arrow-breaking’ interventions. Hausman & Woodward's argument can be rendered valid through a (...)
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  29. Daniel Steel (2006). Homogeneity, Selection, and the Faithfulness Condition. Minds and Machines 16 (3):303-317.
    The faithfulness condition (FC) is a useful principle for inferring causal structure from statistical data. The usual motivation for the FC appeals to theorems showing that exceptions to it have probability zero, provided that some apparently reasonable assumptions obtain. However, some have objected that, the theorems notwithstanding, exceptions to the FC are probable in commonly occurring circumstances. I argue that exceptions to the FC are probable in the circumstances specified by this objection only given the presence of a condition that (...)
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  30. Daniel Steel (2006). Methodological Individualism, Explanation, and Invariance. Philosophy of the Social Sciences 36 (4):440-463.
    This article examines methodological individualism in terms of the theory that invariance under intervention is the signal feature of generalizations that serve as a basis for causal explanation. This theory supports the holist contention that macro-level generalizations can explain, but it also suggests a defense of methodological individualism on the grounds that greater range of invariance under intervention entails deeper explanation. Although this individualist position is not threatened by multiple-realizability, an argument for it based on rational choice theory is called (...)
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  31. Daniel Steel (2005). Mechanisms and Functional Hypotheses in Social Science. Philosophy of Science 72 (5):941-952.
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  32. Daniel Steel (2005). The Facts of the Matter: A Discussion of Norton's Material Theory of Induction. Philosophy of Science 72 (1):188-197.
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  33. Daniel Steel (2005). Indeterminism and the Causal Markov Condition. British Journal for the Philosophy of Science 56 (1):3-26.
    The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyclic, deterministic causal system in which the exogenous variables are independent—can be extended to the indeterministic case. (...)
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  34. Daniel Steel (2004). Can a Reductionist Be a Pluralist? Biology and Philosophy 19 (1):55-73.
    Pluralism is often put forth as a counter-position to reductionism. In this essay, I argue that reductionism and pluralism are in fact consistent. I propose that there are several potential goals for reductions and that the proper form of a reduction should be considered in tandem with the goal that it aims to achieve. This insight provides a basis for clarifying what version(s) of reductionism are currently defended, for explicating the notion of a fundamental level of explanation, and for showing (...)
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  35. Daniel Steel (2004). Social Mechanisms and Causal Inference. Philosophy of the Social Sciences 34 (1):55-78.
    Several authors have claimed that mechanisms play a vital role in distinguishing between causation and mere correlation in the social sciences. Such claims are sometimes interpreted to mean that without mechanisms, causal inference in social science is impossible. The author agrees with critics of this proposition but explains how the account of how mechanisms aid causal inference can be interpreted in a way that does not depend on it. Nevertheless, he shows that this more charitable version of the account is (...)
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  36. Daniel Steel (2003). A Bayesian Way to Make Stopping Rules Matter. Erkenntnis 58 (2):213--227.
    Disputes between advocates of Bayesians and more orthodox approaches to statistical inference presuppose that Bayesians must regard must regard stopping rules, which play an important role in orthodox statistical methods, as evidentially irrelevant.In this essay, I show that this is not the case and that the stopping rule is evidentially relevant given some Bayesian confirmation measures that have been seriously proposed. However, I show that accepting a confirmation measure of this sort comes at the cost of rejecting two useful ancillaryBayesian (...)
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  37. Daniel Steel (2003). Making Time Stand Still: A Response to Sober's Counter-Example to the Principle of the Common Cause. British Journal for the Philosophy of Science 54 (2):309-317.
    In a recent article, Elliot Sober responds to challenges to a counter-example that he posed some years earlier to the Principle of the Common Cause (PCC). I agree that Sober has indeed produced a genuine counter-example to the PCC, but argue against the methodological moral that Sober wishes to draw from it. Contrary to Sober, I argue that the possibility of exceptions to the PCC does not undermine its status as a central assumption for methods that endeavor to draw causal (...)
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  38. Daniel Steel (2001). Bayesian Statistics in Radiocarbon Calibration. Proceedings of the Philosophy of Science Association 2001 (3):S153-.
    Critics of Bayesianism often assert that scientists are not Bayesians. The widespread use of Bayesian statistics in the field of radiocarbon calibration is discussed in relation to this charge. This case study illustrates the willingness of scientists to use Bayesian statistics when the approach offers some advantage, while continuing to use orthodox methods in other contexts. The case of radiocarbon calibration, therefore, suggests a picture of statistical practice in science as eclectic and pragmatic rather than rigidly adhering to any one (...)
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  39. Daniel Steel (1998). A Reply to Jones. Philosophy of Science 65 (4):682-687.
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  40. Daniel Steel (1998). Warfare and Western Manufactures: A Case Study of Explanation in Anthropology. Philosophy of Science 65 (4):649-671.
    I use an explanation of Yanomami warfare given by the anthropologist Brian Ferguson as a case study to compare the merits of the causal and unification approaches to explanation. I argue that Ferguson's insistence on explaining actual occurrences and patterns of Yanomami warfare together with his claim that all of his generalizations are statistical raises difficulties for the unification approach, because of its commitment to "deductive chauvinism." Moreover, I show that there are serious difficulties involved in comparing the "unifying power" (...)
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  41. Daniel Steel (1996). Bayesianism and the Value of Diverse Evidence. Philosophy of Science 63 (4):666-674.
    In a recent essay (1995), Andrew Wayne charges that Bayesian attempts to account for the rule that, ceteris paribus, diverse evidence confirms better than narrow evidence are inadequate. I reply to these criticisms and argue that, on the contrary, one of the Bayesian approaches considered by Wayne does an excellent job of explaining why, and under what circumstances, diverse evidence is valuable.
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