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  1. 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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  2. 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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  3.  54
    Causality: Philosophical Theory Meets Scientific Practice.Phyllis Illari & Federica Russo - 2014 - Oxford, UK: 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.  32
    Evaluating Evidence of Mechanisms in Medicine.Veli-Pekka Parkkinen, Christian Wallmann, Michael Wilde, Brendan Clarke, Phyllis Illari, Michael P. Kelly, Charles Norell, Federica Russo, Beth Shaw & Jon Williamson - 2018 - Dordrecht, Netherlands: Springer.
    The use of evidence in medicine is something we should continuously seek to improve. This book seeks to develop our understanding of evidence of mechanism in evaluating evidence in medicine, public health, and social care; and also offers tools to help implement improved assessment of evidence of mechanism in practice. In this way, the book offers a bridge between more theoretical and conceptual insights and worries about evidence of mechanism and practical means to fit the results into evidence assessment procedures.
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  5. Causality and Causal Modelling in the Social Sciences.Federica Russo - 2009 - Springer, Dordrecht.
    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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  6.  4
    Critical Data Studies: An Introduction.Federica Russo & Andrew Iliadis - 2016 - Big Data and Society 3 (2).
    Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...)
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  7.  52
    The Evidence That Evidence-Based Medicine Omits.Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson - unknown
    According to current hierarchies of evidence for EBM, evidence of correlation is always more important than evidence of mechanisms when evaluating and establishing causal claims. We argue that evidence of mechanisms needs to be treated alongside evidence of correlation. This is for three reasons. First, correlation is always a fallible indicator of causation, subject in particular to the problem of confounding; evidence of mechanisms can in some cases be more important than evidence of correlation when assessing a causal claim. Second, (...)
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  8.  83
    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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  9.  49
    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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  10.  66
    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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  11.  75
    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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  12. 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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  13.  5
    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.
  14.  48
    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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  15. Structural Modelling, Exogeneity, and Causality.Federica Russo, Michel Mouchart & Guillaume Wunsch - 2009 - In Causal Analysis in Population Studies. pp. 59-82.
    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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  16.  23
    Models for Prediction, Explanation and Control: Recursive Bayesian Networks.Lorenzo Casini, Phyllis McKay Illari, Federica Russo & Jon Williamson - 2011 - Theoria : An International Journal for Theory, History and Fundations of Science 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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  17.  15
    Epistemic Diversity and the Question of Lingua Franca in Science and Philosophy.Federico Gobbo & Federica Russo - 2020 - Foundations of Science 25 (1):185-207.
    Epistemic diversity is the ability or possibility of producing diverse and rich epistemic apparati to make sense of the world around us. In this paper we discuss whether, and to what extent, different conceptions of knowledge—notably as ‘justified true belief’ and as ‘distributed and embodied cognition’—hinder or foster epistemic diversity. We then link this discussion to the widespread move in science and philosophy towards monolingual disciplinary environments. We argue that English, despite all appearance, is no Lingua Franca, and we give (...)
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  18.  22
    Causality in Cancer Research: A Journey Through Models in Molecular Epidemiology and Their Philosophical Interpretation.Paolo Vineis, Phyllis Illari & Federica Russo - 2017 - Emerging Themes in Epidemiology 14 (7):1-8.
    In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making diferent traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing—notably, the “sufcient-component-cause framework” and the “mark transmission” approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics (...)
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  19.  86
    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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  20.  17
    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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  21.  36
    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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  22.  66
    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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  23.  97
    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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  24.  21
    Reconstructing the Mixed Mechanisms of Health: The Role of Bio- and Socio-Markers.Virginia Ghiara & Federica Russo - unknown
    It is widely agreed that social factors are related to health outcomes: much research served to establish correlations between classes of social factors on the one hand and classes of disease on the other hand. However, why and how social factors are an active part in the aetiology of disease development is something that is gaining attention only recently in the health sciences and in the medical humanities. In this paper, we advance the view that, just as bio-markers help trace (...)
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  25.  63
    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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  26.  29
    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.
  27.  36
    On Empirical Generalisations.Federica Russo - 2012 - In Probabilities, Laws, and Structures. pp. 123-139.
    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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  28.  39
    Philosophy of Medicine: Between Clinical Trials and Mechanisms: Jeremy Howick: The Philosophy of Evidence-Based Medicine. Oxford: Wiley-Blackwell, 2011, 248pp, £37.99/€45.60 PB. [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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  29. 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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  30.  20
    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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  31.  23
    Causal Arrows in Econometric Models.Federica Russo - 2009 - Humana Mente 3 (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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  32. Causality in the Sciences.Phyllis Illari, Federica Russo & Jon Williamson (eds.) - 2011 - Oxford University Press.
    Why do ideas of how mechanisms relate to causality and probability differ so much across the sciences? Can progress in understanding the tools of causal inference in some sciences lead to progress in others? This book tackles these questions and others concerning the use of causality in the sciences.
     
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  33.  13
    Editors’ Letter.Phyllis Illari & Federica Russo - 2017 - European Journal for Philosophy of Science 7 (3):391-392.
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  34.  20
    Editors’ Letter.Phyllis Kirstin Illari & Federica Russo - 2018 - European Journal for Philosophy of Science 8 (1):1-2.
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  35.  21
    Editors’ Letter.Phyllis Kirstin Illari & Federica Russo - 2018 - European Journal for Philosophy of Science 8 (3):307-308.
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  36.  47
    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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  37. 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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  38.  1
    COVID-19 Heralds a New Epistemology of Science for the Public Good.Manfred D. Laubichler, Peter Schlosser, Jürgen Renn, Federica Russo, Gerald Steiner, Eva Schernhammer, Carlo Jaeger & Guido Caniglia - 2021 - History and Philosophy of the Life Sciences 43 (2):1-6.
    COVID-19 has revealed that science needs to learn how to better deal with the irreducible uncertainty that comes with global systemic risks as well as with the social responsibility of science towards the public good. Further developing the epistemological principles of new theories and experimental practices, alternative investigative pathways and communication, and diverse voices can be an important contribution of history and philosophy of science and of science studies to ongoing transformations of the scientific enterprise.
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  39.  34
    Scientific Disagreement and Evidential Pluralism: Lessons From the Studies on Hypercholesterolemia.Veli-Pekka Parkkinen, Federica Russo & Christian Wallmann - 2017 - Humana Mente 10 (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. In such cases, it is preferable to talk about disagreement, rather than inconsistency. Disagreement (...)
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  40.  21
    A Pluralist Account of Causality: Peter V. Rabins: The Why of Things: Causality in Science, Medicine, and Life. New York: Columbia University Press, 2013, 304pp, $28.95, £19.95 HB.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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  41.  4
    Causality and Modelling in the Sciences: Introduction.Federica Russo & María Jiménez-Buedo - 2017 - Disputatio 9 (47):423-427.
    The advantage of examining causality from the perspective of modelling is thus that it puts us naturally closer to the practice of the sciences. This means being able to set up an interdisciplinary dialogue that contrasts and compares modelling practices in different fields, say economics and biology, medicine and statistics, climate change and physics. It also means that it helps philosophers looking for questions that go beyond the narrow ‘what-is-causality’ or ‘what-are-relata’ and thus puts causality right at the centre of (...)
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  42. Causality and Probability in the Sciences.Federica Russo & Jon Williamson (eds.) - 2007 - College Publications.
    Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, make use of probability and statistics to infer causal relationships. The social and health sciences analyse population-level data using statistical methods to infer average causal relations. In diagnosis of disease, probabilistic statements are based on population-level causal knowledge combined with knowledge of a particular person’s symptoms. For the physical sciences, the Salmon-Dowe account develops an (...)
     
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  43.  6
    Can a Unified Approach Help in Teaching Philosophy of Science?Federica Russo - 2016 - Science & Education 25 (7-8):929-931.
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  44.  23
    Comparative Process Tracing: Yet Another Virtue of Mechanisms?Federica Russo - 2010 - Journal of Economic Methodology 17 (1):81-87.
  45.  27
    Depth. An Account of Scientific Explanations. [REVIEW]Federica Russo - 2008 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (2):261-263.
  46.  15
    Digital Technologies, Ethical Questions, and the Need of an Informational Framework.Federica Russo - 2018 - Philosophy and Technology 31 (4):655-667.
    Technologies have always been bearers of profound changes in science, society, and any other aspect of life. The latest technological revolution—the digital revolution—is no exception in this respect. This paper presents the revolution brought about by digital technologies through the lenses of a specific approach: the philosophy of information. It is argued that the adoption of an informational approach helps avoiding utopian or dystopian approaches to technology, both expressions of technological determinism. Such an approach provides a conceptual framework able to (...)
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  47.  10
    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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  48.  21
    Kevin C. Elliott and Ted Richards , Exploring Inductive Risk. Case Studies of Values in Science.Federica Russo - forthcoming - Hopos: The Journal of the International Society for the History of Philosophy of Science.
  49.  12
    Kevin C. Elliott and Ted Richards, Eds. Exploring Inductive Risk: Case Studies of Values in Science. New York: Oxford University Press, 2017. Pp. Xiv+277. $99.00 ; $40.00. [REVIEW]Federica Russo - 2019 - Hopos: The Journal of the International Society for the History of Philosophy of Science 9 (1):179-182.
  50.  6
    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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