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376Models for prediction, explanation and control: recursive bayesian networksTheoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (1): 5-33. 2011.The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation an…Read more
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28For my own work in philosophy of science, I find of utmost importance to exchange ideas with practicing scientists. The author of this book, Peter Rabins, is a medical doctor specializing in psychiatry. With much regret, I have not met Professor Rabins in person yet, but I’m hoping to do so soon, as his recent book The Why of Things: Causality in Science, Medicine, and Life has been a most enjoyable read and source of inspiration. The book constitutes a noteworthy addition to Professor Rabins’ a…Read more
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32IntroductionStudies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (4): 758-760. 2012.
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562The Rationale of Variation in Methodological and Evidential PluralismPhilosophica 77 (1). 2006.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 dont entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, bas…Read more
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276Mechanisms and the Evidence HierarchyTopoi 33 (2): 339-360. 2014.Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mecha…Read more
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79Information Channels and Biomarkers of DiseaseTopoi 35 (1): 175-190. 2016.Current research in molecular epidemiology uses biomarkers to model the different disease phases from environmental exposure, to early clinical changes, to development of disease. The hope is to get a better understanding of the causal impact of a number of pollutants and chemicals on several diseases, including cancer and allergies. In a recent paper Russo and Williamson address the question of what evidential elements enter the conceptualisation and modelling stages of this type of biomarkers …Read more
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76Causal models and evidential pluralism in econometricsJournal of Economic Methodology 21 (1): 54-76. 2014.Social research, from economics to demography and epidemiology, makes extensive use of statistical models in order to establish causal relations. The question arises as to what guarantees the causal interpretation of such models. In this paper we focus on econometrics and advance the view that causal models are ‘augmented’ statistical models that incorporate important causal information which contributes to their causal interpretation. The primary objective of this paper is to argue that causal …Read more
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68Introduction: Evidence and Causality in the SciencesTopoi 33 (2): 293-294. 2014.Evidence and CausalityCausality is a vibrant and thriving topic in philosophy of science. It is closely related to many other challenging scientific concepts, such as probability and mechanisms, which arise in many different scientific contexts, in different fields. For example, probability and mechanisms are relevant to both causal inference (finding out what causes what) and causal explanation (explaining how a cause produces its effect). They are also of interest to fields as diverse as astro…Read more
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80Are causal analysis and system analysis compatible approaches?International Studies in the Philosophy of Science 24 (1). 2010.In social science, one objection to causal analysis is that the assumption of the closure of the system makes the analysis too narrow in scope, that is, it considers only 'closed' and 'hermetic' systems thus neglecting many other external influences. On the contrary, system analysis deals with complex structures where every element is interrelated with everything else in the system. The question arises as to whether the two approaches can be compatible and whether causal analysis can be integrat…Read more
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102Causality in the sciences (edited book)Oxford University Press. 2011.The book tackles these questions as well as others concerning the use of causality in the sciences.