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  1. 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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  2. Federica Russo, Structural Modelling, Exogeneity, and Causality.
    This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based which is based on (i) congruence with background knowledge, (ii) invariance under a large variety of (...)
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  3. Phyllis Illari & Federica Russo (forthcoming). Information Channels and Biomarkers of Disease. Topoi:1-16.
    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 (Med Stud, 2012) address the question of what evidential elements enter the conceptualisation and modelling stages of this type of biomarkers research. Recent research (...)
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  4. Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson (2014). Mechanisms and the Evidence Hierarchy. Topoi 33 (2):339-360.
    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 mechanisms alongside evidence of correlation in (...)
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  5. Phyllis Illari & Federica Russo (2014). Introduction: Evidence and Causality in the Sciences. Topoi 33 (2):293-294.
    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 astrophysics, biochemistry, biomedical and social sciences. At (...)
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  6. Alessio Moneta & Federica Russo (2014). Causal Models and Evidential Pluralism in Econometrics. Journal of Economic Methodology 21 (1):54-76.
  7. Federica Russo (2014). What Invariance Is and How to Test for It. International Studies in the Philosophy of Science 28 (2):157-183.
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  8. Guillaume Wunsch, Michel Mouchart & Federica Russo (2014). Functions and Mechanisms in Structural-Modelling Explanations. Journal for General Philosophy of Science 45 (1):187-208.
    One way social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. Explanations in social sciences share important aspects that have been highlighted in the mechanisms literature. Notably, spelling out the functioning the mechanism gives it explanatory power. Thus social scientists should choose the variables to include in the model on the basis of their function (...)
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  9. Phyllis Illari, Julian Reiss & Federica Russo (2012). Introduction. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (4):758-760.
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  10. Federica Russo (2012). Public Health Policy, Evidence, and Causation: Lessons From the Studies on Obesity. Medicine, Health Care and Philosophy 15 (2):141-151.
    The paper addresses the question of how different types of evidence ought to inform public health policy. By analysing case studies on obesity, the paper draws lessons about the different roles that different types of evidence play in setting up public health policies. More specifically, it is argued that evidence of difference-making supports considerations about ‘what works for whom in what circumstances’, and that evidence of mechanisms provides information about the ‘causal pathways’ to intervene upon.
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  11. Federica Russo (2012). Philosophy of Medicine: Between Clinical Trials and Mechanisms. [REVIEW] Metascience 21 (2):387-390.
    Philosophy of medicine: between clinical trials and mechanisms Content Type Journal Article Category Book Review Pages 1-4 DOI 10.1007/s11016-011-9630-5 Authors Federica Russo, Philosophy-SECL, University of Kent, Canterbury, CT2 7NF UK Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.
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  12. Federica Russo & Jon Williamson (2012). EnviroGenomarkers: The Interplay Between Mechanisms and Difference Making in Establishing Causal Claims. Medicine Studies 3 (4):249-262.
    According to Russo and Williamson (Int Stud Philos Sci 21(2):157–170, 2007, Hist Philos Life Sci 33:389–396, 2011a, Philos Sci 1(1):47–69, 2011b), in order to establish a causal claim of the form, ‘C is a cause of E’, one typically needs evidence that there is an underlying mechanism between C and E as well as evidence that C makes a difference to E. This thesis has been used to argue that hierarchies of evidence, as championed by evidence-based movements, tend to give (...)
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  13. Lorenzo Casini, Phyllis Mckay Illari, Federica Russo & Jon Williamson (2011). Models for Prediction, Explanation and Control. Theoria 26 (1):5-33.
    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 and control respectively, an RBN can be applied to all these tasks. We show in particular (...)
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  14. Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.) (2011). Causality in the Sciences. Oxford University Press.
    The book tackles these questions as well as others concerning the use of causality in the sciences.
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  15. Phyllis McKay Illari, Federica Russo & Jon Williamson (2011). Why Look at Causality in the Sciences? In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oup Oxford.
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  16. Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.) (2011). Causality in the Sciences. Oxford University Press.
    There is a need for integrated thinking about causality, probability and mechanisms in scientific methodology. Causality and probability are long-established central concepts in the sciences, with a corresponding philosophical literature examining their problems. On the other hand, the philosophical literature examining mechanisms is not long-established, and there is no clear idea of how mechanisms relate to causality and probability. But we need some idea if we are to understand causal inference in the sciences: a panoply of disciplines, ranging from epidemiology (...)
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  17. Michel Mouchart & Federica Russo (2011). Causal Explanation: Recursive Decompositions and Mechanisms. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oup Oxford.
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  18. Federica Russo (2011). Correlational Data, Causal Hypotheses, and Validity. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 42 (1):85 - 107.
    A shared problem across the sciences is to make sense of correlational data coming from observations and/or from experiments. Arguably, this means establishing when correlations are causal and when they are not. This is an old problem in philosophy. This paper, narrowing down the scope to quantitative causal analysis in social science, reformulates the problem in terms of the validity of statistical models. Two strategies to make sense of correlational data are presented: first, a 'structural strategy', the goal of which (...)
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  19. Federica Russo (2011). Depth. An Account of Scientific Explanations. Theoria 26 (2):261-263.
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  20. Federica Russo & Jon Williamson (2011). Epistemic Causality and Evidence-Based Medicine. History and Philosophy of the Life Sciences 33 (4).
    Causal claims in biomedical contexts are ubiquitous albeit they are not always made explicit. This paper addresses the question of what causal claims mean in the context of disease. It is argued that in medical contexts causality ought to be interpreted according to the epistemic theory. The epistemic theory offers an alternative to traditional accounts that cash out causation either in terms of “difference-making” relations or in terms of mechanisms. According to the epistemic approach, causal claims tell us about which (...)
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  21. Federica Russo & Jon Williamson (2011). Generic Versus Single-Case Causality: The Case of Autopsy. [REVIEW] European Journal for Philosophy of Science 1 (1):47-69.
    This paper addresses questions about how the levels of causality (generic and single-case causality) are related. One question is epistemological: can relationships at one level be evidence for relationships at the other level? We present three kinds of answer to this question, categorised according to whether inference is top-down, bottom-up, or the levels are independent. A second question is metaphysical: can relationships at one level be reduced to relationships at the other level? We present three kinds of answer to this (...)
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  22. Federica Russo (2010). Are Causal Analysis and System Analysis Compatible Approaches? International Studies in the Philosophy of Science 24 (1):67 – 90.
    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 integrated into the (...)
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  23. Federica Russo (2010). Comparative Process Tracing: Yet Another Virtue of Mechanisms? Journal of Economic Methodology 17 (1):81-87.
  24. Federica Russo (2010). Representation and Structure in Economics. The Methodology of Econometric Models of the Consumption Function , Hsiang-Ke Chao. Routledge, 2009, XIV + 161 Pages. [REVIEW] Economics and Philosophy 26 (1):114-118.
  25. Federica Russo (2009). Variational Causal Claims in Epidemiology. Perspectives in Biology and Medicine 52 (4):540-554.
    The paper examines definitions of ‘cause’ in the epidemiological literature. Those definitions all describe causes as factors that make a difference to the distribution of disease or to individual health status. In the philosophical jargon, causes in epidemiology are difference-makers. Two claims are defended. First, it is argued that those definitions underpin an epistemology and a methodology that hinge upon the notion of variation, contra the dominant Humean paradigm according to which we infer causality from regularity. Second, despite the fact (...)
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  26. Federica Russo & Jon Williamson (eds.) (2007). Causality and Probability in the Sciences.
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  27. Federica Russo & Jon Williamson (2007). Interpreting Causality in the Health Sciences. International Studies in the Philosophy of Science 21 (2):157 – 170.
    We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms, and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences - pluralism about causality will not do either. Instead, we maintain, the health sciences require a theory (...)
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  28. Federica Russo & Jon Williamson (2007). Interpreting Probability in Causal Models for Cancer. In Federica Russo & Jon Williamson (eds.), Causality and Probability in the Sciences. 217--242.
    How should probabilities be interpreted in causal models in the social and health sciences? In this paper we take a step towards answering this question by investigating the case of cancer in epidemiology and arguing that the objective Bayesian interpretation is most appropriate in this domain.
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  29. 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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  30. Federica Russo (2006). Salmon and Van Fraassen on the Existence of Unobservable Entities: A Matter of Interpretation of Probability. [REVIEW] Foundations of Science 11 (3):221-247.
    A careful analysis of Salmon’s Theoretical Realism and van Fraassen’s Constructive Empiricism shows that both share a common origin: the requirement of literal construal of theories inherited by the Standard View. However, despite this common starting point, Salmon and van Fraassen strongly disagree on the existence of unobservable entities. I argue that their different ontological commitment towards the existence of unobservables traces back to their different views on the interpretation of probability via different conceptions of induction. In fact, inferences to (...)
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  31. Federica Russo, On The Foundations Of Agency-Manipulability Theories Of Causation.
    The Agency and the Manipulability theory of causation, in spite of significant differences, share at least three claims. First, that manipulation – roughly, that by manipulating causes we bring about effects – is a central notion for causation; second, that such a notion of manipulation allows a reductive – i.e. general and comprehensive – account of causation; third, that this view has its forefathers in the works of Collingwood, Gasking and von Wright. This paper mainly challenges the third claim and (...)
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  32. Federica Russo (2003). Jean-René Vernes, L'existence du monde extérieur et l'erreur du rationalisme. Revue Philosophique De Louvain 101 (1):173-176.
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  33. Federica Russo (2003). Élie Zahar, Essai d'épistémologie réaliste. Avant-propos de Alain Boyer. Revue Philosophique De Louvain 101 (3):516-519.
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  34. Federica Russo (2003). The Reality of the Unobservable. Observability, Unobservability and Their Impact on the Issue of Scientific Realism. Edited by Evandro Agazzi and Massimo Pauri. [REVIEW] Revue Philosophique De Louvain 101 (1):176-179.
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