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Profile: Federica Russo (University of Kent, University of Amsterdam)
Profile: Federica Russo
  1. Interpreting Causality in the Health Sciences.Federica Russo & Jon Williamson - 2007 - 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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  2. Mechanisms and the Evidence Hierarchy.Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson - 2014 - 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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  3.  16
    Causality: Philosophical Theory Meets Scientific Practice.Phyllis Illari & Federica Russo - 2014 - Oxford University Press.
    Scientific and philosophical literature on causality has become highly specialised. It is hard to find suitable access points for students, young researchers, or professionals outside this domain. This book provides a guide to the complex literature, explains the scientific problems of causality and the philosophical tools needed to address them.
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  4.  46
    Epistemic Causality and Evidence-Based Medicine.Federica Russo & Jon Williamson - 2011 - 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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  5. Models for Prediction, Explanation and Control.Lorenzo Casini, Phyllis Mckay Illari, Federica Russo & Jon Williamson - 2011 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 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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  6. Structural Modelling, Exogeneity, and Causality.Federica Russo - unknown
    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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  7. The Rationale of Variation in Methodological and Evidential Pluralism.Federica Russo - 2006 - 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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  8.  52
    Causality in the Sciences.Illari Phyllis McKay, Russo Federica & Williamson Jon (eds.) - 2011 - Oxford University Press.
    The book tackles these questions as well as others concerning the use of causality in the sciences.
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  9. Interpreting Probability in Causal Models for Cancer.Federica Russo & Jon Williamson - 2007 - In Federica Russo & Jon Williamson (eds.), Causality and Probability in the Sciences. pp. 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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  10.  44
    EnviroGenomarkers: The Interplay Between Mechanisms and Difference Making in Establishing Causal Claims.Federica Russo & Jon Williamson - 2012 - Medicine Studies 3 (4):249-262.
    According to Russo and Williamson :157–170, 2007, Hist Philos Life Sci 33:389–396, 2011a, Philos Sci 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 primacy to evidence of (...)
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  11. Causal Explanation: Recursive Decompositions and Mechanisms.Michel Mouchart & Federica Russo - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
     
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  12.  35
    Correlational Data, Causal Hypotheses, and Validity.Federica Russo - 2011 - 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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  13. On The Foundations Of Agency-Manipulability Theories Of Causation.Federica Russo - unknown
    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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  14.  52
    Causal Webs in Epidemiology.Federica Russo - unknown
    The notion of ‘causal web’ emerged in the epidemiological literature in the early Sixties and had to wait until the Nineties for a thorough critical appraisal. Famously, Nancy Krieger argued that such a notion isn’t helpful unless we specify what kind of spiders create the webs. This means, according to Krieger, (i) that the role of the spiders is to provide an explanation of the yarns of the web and (ii) that the sought spiders have to be biological and social. (...)
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  15.  4
    Editors’ Letter.Phyllis Illari & Federica Russo - 2017 - European Journal for Philosophy of Science 7 (3):391-392.
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  16.  3
    Scientific Disagreement and Evidential Pluralism: Lessons From the Studies on Hypercholesterolemia.Veli-Pekka Parkkinen, Christian Wallmann & Federica Russo - 2017 - Humana Mente 32:75-116.
    Inconsistencies between scientific theories have been studied, by and large, from the perspective of paraconsistent logic. This approach considered the formal properties of theories and the structure of inferences one can legitimately draw from theories. However, inconsistencies can be also analysed from the perspective of modelling practices, in particular how modelling practices may lead scientists to form opinions and attitudes that are different, but not necessarily inconsistent (from a logical point of view). In such cases, it is preferable to talk (...)
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  17.  64
    Causality and Causal Modelling in the Social Sciences.Federica Russo - unknown
    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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  18.  22
    Causal Models and Evidential Pluralism in Econometrics.Alessio Moneta & Federica Russo - 2014 - Journal of Economic Methodology 21 (1):54-76.
    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 claims are established on the basis of a (...)
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  19. Why Look at Causality in the Sciences?Phyllis McKay Illari, Federica Russo & Jon Williamson - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
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  20.  52
    Public Health Policy, Evidence, and Causation: Lessons From the Studies on Obesity.Federica Russo - 2012 - 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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  21. Salmon and Van Fraassen on the Existence of Unobservable Entities: A Matter of Interpretation of Probability. [REVIEW]Federica Russo - 2006 - 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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  22.  61
    Variational Causal Claims in Epidemiology.Federica Russo - 2009 - 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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  23.  39
    Are Causal Analysis and System Analysis Compatible Approaches?Federica Russo - 2010 - 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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  24. Causality and Probability in the Sciences.Federica Russo & Jon Williamson (eds.) - 2007
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  25.  33
    The Reality of the Unobservable. Observability, Unobservability and Their Impact on the Issue of Scientific Realism. Edited by Evandro Agazzi and Massimo Pauri. [REVIEW]Federica Russo - 2003 - Revue Philosophique De Louvain 101 (1):176-179.
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  26.  23
    Depth. An Account of Scientific Explanations.Federica Russo - 2011 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (2):261-263.
  27. Neuroeconomics: Hype or Hope: Rotterdam, 20‐22 November, 2008.Federica Russo - 2009 - Humana Mente 10.
  28.  16
    On Empirical Generalisations.Federica Russo - unknown
    Manipulationism holds that information about the results of interventions is of utmost importance for scientific practices such as causal assessment or explanation. Specifically, manipulation provides information about the stability, or invariance, of the relationship between X and Y: were we to wiggle the cause X, the effect Y would accordingly wiggle and, additionally, the relation between the two will not be disrupted. This sort of relationship between variables are called 'invariant empirical generalisations'. The paper focuses on questions about causal assessment (...)
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  29.  16
    Strevens. 2009. Depth. An Account of Scientific Explanations.Federica Russo - unknown
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  30.  26
    Introduction: Evidence and Causality in the Sciences.Phyllis Illari & Federica Russo - 2014 - 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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  31.  25
    Information Channels and Biomarkers of Disease.Phyllis Illari & Federica Russo - 2016 - Topoi 35 (1):175-190.
    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 research. Recent research in causality has (...)
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  32.  16
    What Invariance Is and How to Test for It.Federica Russo - 2014 - International Studies in the Philosophy of Science 28 (2):157-183.
    Causal assessment is the problem of establishing whether a relation between (variable) X and (variable) Y is causal. This problem, to be sure, is widespread across the sciences. According to accredited positions in the philosophy of causality and in social science methodology, invariance under intervention provides the most reliable test to decide whether X causes Y. This account of invariance (under intervention) has been criticised, among other reasons, because it makes manipulations on the putative causal factor fundamental for the causal (...)
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  33.  20
    Comparative Process Tracing: Yet Another Virtue of Mechanisms?Federica Russo - 2010 - Journal of Economic Methodology 17 (1):81-87.
  34.  25
    Philosophy of Medicine: Between Clinical Trials and Mechanisms. [REVIEW]Federica Russo - 2012 - 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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  35.  13
    Functions and Mechanisms in Structural-Modelling Explanations.Guillaume Wunsch, Michel Mouchart & Federica Russo - 2014 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 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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  36.  3
    Models for Prediction, Explanation and Control.Lorenzo Casini, Phyllis Mckay Illari, Federica Russo & Jon Williamson - 2011 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (1):5-33.
    The Recursive Bayesian Net 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 how (...)
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  37.  8
    Frequency-Driven Probabilities In Quantitative Causal Analysis.Federica Russo - 2006 - Philosophical Writings 32 (2).
    This paper addresses the problem of the interpretation of probability in quantitative causal analysis. I argue that probability has to be interpreted according to a Bayesian framework in which degrees of belief are frequency-driven. This interpretation can account for the peculiar use and meaning of probability in generic and single-case causal inferences involved in this domain.
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  38.  4
    Combining Probability and Logic.Fabio Cozman, Rolf Haenni, Jan-Willem Romeijn, Federica Russo, Gregory Wheeler & Jon Williamson - 2009 - Journal of Applied Logic 7 (2):131-135.
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  39.  8
    A Pluralist Account of Causality.Federica Russo - 2015 - Metascience 24 (3):381-384.
    For 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 (...)
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  40.  14
    Introduction.Phyllis Illari, Julian Reiss & Federica Russo - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (4):758-760.
  41.  20
    Representation and Structure in Economics. The Methodology of Econometric Models of the Consumption Function , Hsiang-Ke Chao. Routledge, 2009, XIV + 161 Pages. [REVIEW]Federica Russo - 2010 - Economics and Philosophy 26 (1):114-118.
  42.  3
    Jean-René Vernes, L'existence du monde extérieur et l'erreur du rationalisme.Federica Russo - 2003 - Revue Philosophique De Louvain 101 (1):173-176.
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  43.  1
    Introduction.Phyllis Illari, Julian Reiss & Federica Russo - 2012 - Studies in History and Philosophy of Science Part C 43 (4):758-760.
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  44.  9
    Causal Arrows in Econometric Models.Federica Russo - 2009 - Humana Mente 10.
    Econometrics applies statistical methods to study economic phenomena. Roughly, by means of equations, econometricians typically account for the response variable in terms of a number of explanatory variables. The question arises under what conditions econometric models can be given a causal interpretation. By drawing the distinction between associational models and causal models, the paper argues that a proper use of background knowledge, three distinct types of assumptions (statistical, extra-statistical, and causal), and the hypothetico-deductive methodology provide sufficient conditions for a causal (...)
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  45.  2
    Élie Zahar, Essai d'épistémologie réaliste. Avant-propos de Alain Boyer.Federica Russo - 2003 - Revue Philosophique De Louvain 101 (3):516-519.
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  46.  1
    Representation and Structure in Economics. The Methodology of Econometric Models of the Consumption Function, Chao Hsiang-Ke. Routledge, 2009, Xiv + 161 Pages. [REVIEW]Federica Russo - 2010 - Economics and Philosophy 26 (1):114-118.
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  47.  1
    The Integration of Social, Behavioral, and Biological Mechanisms in Models of Pathogenesis.Michael P. Kelly, Rachel S. Kelly & Federica Russo - 2014 - Perspectives in Biology and Medicine 57 (3):308-328.
    One of the guiding principles of modern medical and health sciences is the discovery and description of the modes of origin and the actions of pathogenic precursors of disease. This principle facilitates the design of interventions to reduce the burden of mortality and morbidity in individuals and populations. This enterprise is challenging because of the complexity of the pathogenic mechanisms involved. Although highly intricate descriptions of these mechanisms have been developed, they have mainly been at the biological level. In this (...)
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  48. Review of Representation and Structure in Economics. The Methodology of Econometric Models of the Consumption Function. [REVIEW]Federica Russo - 2010 - Economics and Philosophy 26 (1):114-118.
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