The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform
Sheldon R. Smith - One of the best experts on this subject based on the ideXlab platform.
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Causation and Its Relation to ‘Causal Laws’
The British Journal for the Philosophy of Science, 2007Co-Authors: Sheldon R. SmithAbstract:Many have found attractive views according to which the veracity of specific Causal judgements is underwritten by general Causal Laws. This paper describes various variants of that view and explores complications that appear when one looks at a certain simple type of example from physics. To capture certain Causal dependencies, physics is driven to look at equations which, I argue, are not Causal Laws. One place where physics is forced to look at such equations (and not the only place) is in its handling of Green's functions which reveal point-wise Causal dependencies. Thus, I claim that there is no simple relationship between Causal dependence and Causal Laws of the sort often pictured. Rather, this paper explores the complexity of the relationship in a certain well-understood case.1Introduction2The Causal Covering-Law Thesis3The Laws of String Motion4Green's Functions and Causation5Green's Functions and Boundary Conditions6The Green's Function as a Violation of the Wave Equation6.1The Green's Function and other Senses of ‘Causal Law’: Temporal Propagation and Local Propagation7The Distributional Wave Equation8Why is not the Green's Function a ‘Causal Law’?9Conclusion
Kazuo Hashimoto - One of the best experts on this subject based on the ideXlab platform.
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schema design for Causal Law mining from incomplete database
Discovery Science, 1999Co-Authors: Kazunori Matsumoto, Kazuo HashimotoAbstract:The paper describes the Causal Law mining from an incomplete database. First we extend the definition of association rules in order to deal with uncertain attribute values in records. As Agrawal's well-know algorithm generates too many irrelevant association rules, a filtering technique based on minimal AIC principle is applied here. The graphic representation of association rules validated by a filter may have directed cycles. The authors propose a method to exclude useless rules with a stochastic test, and to construct Bayesian networks from the remaining rules. Finally, a schem for Causal Law Mining is proposed as an integration of the techniques described in the paper.
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Discovery Science - Schema Design for Causal Law Mining from Incomplete Database
Discovery Science, 1999Co-Authors: Kazunori Matsumoto, Kazuo HashimotoAbstract:The paper describes the Causal Law mining from an incomplete database. First we extend the definition of association rules in order to deal with uncertain attribute values in records. As Agrawal's well-know algorithm generates too many irrelevant association rules, a filtering technique based on minimal AIC principle is applied here. The graphic representation of association rules validated by a filter may have directed cycles. The authors propose a method to exclude useless rules with a stochastic test, and to construct Bayesian networks from the remaining rules. Finally, a schem for Causal Law Mining is proposed as an integration of the techniques described in the paper.
Nancy Cartwright - One of the best experts on this subject based on the ideXlab platform.
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Hunting causes and using them: approaches in philosophy and economics: summary
2010Co-Authors: Nancy CartwrightAbstract:Hunting Causes and Using Them: Approaches in Philosophy and Economics (HC&UT) is about notions of Causality appropriate to the sciences, mostly generic Causal claims (Causal Laws) and especially notions that connect Causality with probability. 1 Most of the work for the book is associated with the project ‘Causality: Metaphysics and Methods’. This project argued that metaphysics – our account of what Causal Laws are or what general Causal claims say – should march hand-in-hand with our ways of establishing them. It should be apparent, given the kind of thing we think Causality is, why our methods are good for finding it. If our metaphysics does not mesh with and underwrite the methods, we are willing to trust, we should be wary of both. Many philosophers nowadays look for a single informative feature that characterizes Causal Laws. HC&UT argues instead for Causal pluralism, for a large variety of kinds of Causal Laws as well as purposes for which we call scientific claims Causal. Correlatively different methods for testing Causal claims are suited to different kinds of Causal Laws. No one analysis is privileged and no methods are universally applicable. Much of the argument for pluralism is provided by authors of different accounts of Causality, who provide intuitively plausible counter-examples to each other. Still, most of these accounts seem adequate for the kinds of examples the authors focus on. From the point of view of HC&UT, these examples involve different kinds of Causal Laws or set Causality to different jobs, and the concomitant characterizing feature marks out this one kind of Causal Law. Importantly for the argument, often we can specify what characteristics a system of Laws should have in order for an account/method pair to be applicable. An example is James Woodward’s level invariance, which I see as a …
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Causal Laws, policy predictions and the need for genuine powers
2007Co-Authors: Nancy CartwrightAbstract:Knowledge of Causal Laws is expensive and hard to come by. But we work hard to get it because we believe that it will reduce contingency in planning policies and in building new technologies: knowledge of Causal Laws allows us to predict reliably what the outcomes will be when we manipulate the factors cited as causes in those Laws. Or do they? This paper will argue that Causal Laws have no special role here. As economists from JS Mill to Robert Lucas and David Hendry stress, along recently with philosophers like James Woodward and Sandra Mitchell, they can do the job only if they are invariant under the manipulations proposed. But then, I shall argue, anything that is invariant under the proposed manipulations will do this job equally well. There seems to be nothing special about Causal-Law knowledge in and of itself that makes it particularly valuable for policy and technology prediction. What seems to matter is invariance alone, not Causality. But what guarantees invariance and how do we know when it will obtain? Here certain kinds of Causal Laws do have a special place – those underwritten either by what I have called ‘nomological machines’ or by what I have called ‘capacities’. Capacities and nomological machines have a double virtue that makes them invaluable for policy planning. First, the Causal Laws they give rise to will be invariant so long as they obtain; and second, they typically have visible markers we can come to recognize that tell us when they obtain. The markers for nomological machines are shakier than those for capacities, though, since capacities are often tied to markers by well-established empirical Laws. Capacities have their own drawback however, which is the final topic of this paper: the Causal Laws that are guaranteed by a capacity connect the obtaining (or triggering) of a capacity with its exercise. But Hume argued (mistakenly I suggest) that no distinction can be made between the obtaining of a power and its exercise.
Kazunori Matsumoto - One of the best experts on this subject based on the ideXlab platform.
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schema design for Causal Law mining from incomplete database
Discovery Science, 1999Co-Authors: Kazunori Matsumoto, Kazuo HashimotoAbstract:The paper describes the Causal Law mining from an incomplete database. First we extend the definition of association rules in order to deal with uncertain attribute values in records. As Agrawal's well-know algorithm generates too many irrelevant association rules, a filtering technique based on minimal AIC principle is applied here. The graphic representation of association rules validated by a filter may have directed cycles. The authors propose a method to exclude useless rules with a stochastic test, and to construct Bayesian networks from the remaining rules. Finally, a schem for Causal Law Mining is proposed as an integration of the techniques described in the paper.
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Discovery Science - Schema Design for Causal Law Mining from Incomplete Database
Discovery Science, 1999Co-Authors: Kazunori Matsumoto, Kazuo HashimotoAbstract:The paper describes the Causal Law mining from an incomplete database. First we extend the definition of association rules in order to deal with uncertain attribute values in records. As Agrawal's well-know algorithm generates too many irrelevant association rules, a filtering technique based on minimal AIC principle is applied here. The graphic representation of association rules validated by a filter may have directed cycles. The authors propose a method to exclude useless rules with a stochastic test, and to construct Bayesian networks from the remaining rules. Finally, a schem for Causal Law Mining is proposed as an integration of the techniques described in the paper.
Jan Baedke - One of the best experts on this subject based on the ideXlab platform.
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exclusions explanations and exceptions on the Causal and Lawlike status of the competitive exclusion principle
Philosophy and Theory in Biology, 2015Co-Authors: Jani Raerinne, Jan BaedkeAbstract:The Lawlike and explanatory status of ecologists’ Competitive Exclusion Principle (CEP) is a debated topic. It has been argued that the CEP is a ceteris paribus Law, a non-Lawlike regularity riddled with exceptions, a tautology, a Causal regularity, and so on. We argue that the CEP is an empirically respectful and testable strict Law that is not riddled with genuine exceptions. Moreover, we argue that the CEP is not a Causal explanans in explanations, because it is a coexistence Law, not a Causal Law. Rather than being an explanans, the CEP acts as a contrastive principle sharpening Causal explanations. These results contrast with previous analyses of the CEP by Eliot (2011) and Weber (1999), which are also discussed. As a more general conclusion, we suggest that accounts of Causal explanation in biology have neglected some of the roles that non-Causal Laws play in restricting, sharpening, and facilitating Causal explanations.