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  1. Data-Centric Biology: A Philosophical Study.Sabina Leonelli - 2016 - London: University of Chicago Press.
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  • Causality: Philosophical theory meets scientific practice.Phyllis McKay Illari & Federica Russo - 2014 - Oxford, UK: Oxford University Press. Edited by Federica Russo.
    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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  • Repertoires: A post-Kuhnian perspective on scientific change and collaborative research.Rachel A. Ankeny & Sabina Leonelli - 2016 - Studies in History and Philosophy of Science Part A 60:18-28.
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  • Probabilistic causation.Christopher Hitchcock - 2008 - Stanford Encyclopedia of Philosophy.
    “Probabilistic Causation” designates a group of theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes change the probabilities of their effects. This article traces developments in probabilistic causation, including recent developments in causal modeling. A variety of issues within, and objections to, probabilistic theories of causation will also be discussed.
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  • 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 (e.g., from RCTs) 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 (...)
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  • Evaluating evidential pluralism in epidemiology: mechanistic evidence in exposome research.Stefano Canali - 2019 - History and Philosophy of the Life Sciences 41 (1):4.
    In current philosophical discussions on evidence in the medical sciences, epidemiology has been used to exemplify a specific version of evidential pluralism. According to this view, known as the Russo–Williamson Thesis, evidence of both difference-making and mechanisms is produced to make causal claims in the health sciences. In this paper, I present an analysis of data and evidence in epidemiological practice, with a special focus on research on the exposome, and I cast doubt on the extent to which evidential pluralism (...)
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  • Integrating data to acquire new knowledge: Three modes of integration in plant science.Sabina Leonelli - 2013 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4):503-514.
    This paper discusses what it means and what it takes to integrate data in order to acquire new knowledge about biological entities and processes. Maureen O’Malley and Orkun Soyer have pointed to the scientific work involved in data integration as important and distinct from the work required by other forms of integration, such as methodological and explanatory integration, which have been more successful in captivating the attention of philosophers of science. Here I explore what data integration involves in more detail (...)
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  • Follow *the* science? On the marginal role of the social sciences in the COVID-19 pandemic.Simon Lohse & Stefano Canali - 2021 - European Journal for Philosophy of Science 11 (4):1-28.
    In this paper, we use the case of the COVID-19 pandemic in Europe to address the question of what kind of knowledge we should incorporate into public health policy. We show that policy-making during the COVID-19 pandemic has been biomedicine-centric in that its evidential basis marginalised input from non-biomedical disciplines. We then argue that in particular the social sciences could contribute essential expertise and evidence to public health policy in times of biomedical emergencies and that we should thus strive for (...)
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  • Aspects of Theory-Ladenness in Data-Intensive Science.Wolfgang Pietsch - 2015 - Philosophy of Science 82 (5):905-916.
    Recent claims, mainly from computer scientists, concerning a largely automated and model-free data-intensive science have been countered by critical reactions from a number of philosophers of science. The debate suffers from a lack of detail in two respects, regarding the actual methods used in data-intensive science and the specific ways in which these methods presuppose theoretical assumptions. I examine two widely-used algorithms, classificatory trees and non-parametric regression, and argue that these are theory-laden in an external sense, regarding the framing of (...)
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  • Philosophy of Molecular Medicine: Foundational Issues in Research and Practice.Giovanni Boniolo & Marco J. Nathan (eds.) - 2016 - New York: Routledge.
    _Philosophy of Molecular Medicine: Foundational Issues in Theory and Practice_ aims at a systematic investigation of a number of foundational issues in the field of molecular medicine. The volume is organized around four broad modules focusing, respectively, on the following key aspects: What are the nature, scope, and limits of molecular medicine? How does it provide explanations? How does it represent and model phenomena of interest? How does it infer new knowledge from data and experiments? The essays collected here, authored (...)
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  • Big Data Biology: Between Eliminative Inferences and Exploratory Experiments.Emanuele Ratti - 2015 - Philosophy of Science 82 (2):198-218.
    Recently, biologists have argued that data - driven biology fosters a new scientific methodology; namely, one that is irreducible to traditional methodologies of molecular biology defined as the discovery strategies elucidated by mechanistic philosophy. Here I show how data - driven studies can be included into the traditional mechanistic approach in two respects. On the one hand, some studies provide eliminative inferential procedures to prioritize and develop mechanistic hypotheses. On the other, different studies play an exploratory role in providing useful (...)
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  • Koch’s postulates: An interventionist perspective.Lauren N. Ross & James F. Woodward - 2016 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 59:35-46.
    We argue that Koch’s postulates are best understood within an interventionist account of causation, in the sense described in Woodward. We show how this treatment helps to resolve interpretive puzzles associated with Koch’s work and how it clarifies the different roles the postulates play in providing useful, yet not universal criteria for disease causation. Our paper is an effort at rational reconstruction; we attempt to show how Koch’s postulates and reasoning make sense and are normatively justified within an interventionist framework (...)
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  • Introduction to the Special Theme: The expansion of the health data ecosystem – Rethinking data ethics and governance.Federica Lucivero & Tamar Sharon - 2019 - Big Data and Society 6 (2).
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  • Making evidential claims in epidemiology: Three strategies for the study of the exposome.Stefano Canali - 2020 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 82:101248.
    How is scientific data used to represent phenomena and as evidence for claims about phenomena? In this paper, I propose that a specific type of claims – evidential claims – is involved in data practices to define and restrict the representational and evidential content of a dataset. I present an account of data practices in the epidemiology of the exposome based on the notion of evidential claims, which helps unpack the approaches, assumptions and warrants that connect different stages of research. (...)
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  • 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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  • What Is New about the Exposome? Exploring Scientific Change in Contemporary Epidemiology.Stefano Canali - 2020 - International Journal of Environmental Research and Public Health 2879 (17).
    In this commentary, I discuss the scientific changes brought by the exposome, asking what is new about this approach and line of research. I place the exposome in a historical perspective, by analyzing the conditions under which the exposome has been conceived, developed and established in the context of contemporary epidemiological research. I argue that the exposome has been developed by transferring approaches, methods and conceptualizations from other lines of research in the life and health sciences. I thus discuss the (...)
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  • 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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  • 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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  • Causation and prediction in epidemiology: A guide to the “Methodological Revolution”.Alex Broadbent - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 54:72-80.
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  • Where health and environment meet: the use of invariant parameters in big data analysis.Sabina Leonelli & Niccolò Tempini - 2018 - Synthese 198 (S10):2485-2504.
    The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups”—that is the linking of data from epidemiology, biomedicine, climate and environmental science, which (...)
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  • The doctrine of specific etiology.Lauren N. Ross - 2018 - Biology and Philosophy 33 (5-6):37.
    Modern medicine is often said to have originated with nineteenth century germ theory, which attributed diseases to bacterial contagions. The success of this theory is often associated with an underlying principle referred to as the “doctrine of specific etiology”. This doctrine refers to specificity at the level of disease causation or etiology. While the importance of this doctrine is frequently emphasized in the philosophical, historical, and medical literature, these sources lack a clear account of the types of specificity that it (...)
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  • The Environment Ontology: Contextualising biological and biomedical entities.Pier Luigi Buttigieg, Norman Morrison, Barry Smith, Christopher J. Mungall & Suzanna E. Lewis - 2013 - Journal of Biomedical Semantics 4 (43):1-9.
    As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s motivation, (...)
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  • A pluralistic and socially responsible philosophy of epidemiology field should actively engage with social determinants of health and health disparities.Sean A. Valles - 2019 - Synthese 198 (Suppl 10):2589-2611.
    Philosophy of epidemiology has recently emerged as a distinct branch of philosophy. The field will surely benefit from pluralism, reflected in the broad range of topics and perspectives in this special issue. Here, I argue that a healthy pluralistic field of philosophy of epidemiology has social responsibilities that require the field as a whole to engage actively with research on social determinants of health and health disparities. Practicing epidemiologists and the broader community of public health scientists have gradually acknowledged that (...)
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  • Mining data, gathering variables and recombining information: the flexible architecture of epidemiological studies.Susanne Bauer - 2008 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 39 (4):415-428.
    Since the second half of the twentieth century, biomedical research has made increasing use of epidemiological methods to establish empirical evidence on a population level. This paper is about practices with data in epidemiological research, based on a case study in Denmark. I propose an epistemology of record linkage that invites exploration of epidemiological studies as heterogeneous assemblages. Focusing on data collecting, sampling and linkage, I examine how data organisation and processing become productive beyond the context of their collection. The (...)
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  • Making Predictions: Computing Populations.Susanne Bauer, Christine Bischof & Christine Holmberg - 2013 - Science, Technology, and Human Values 38 (3):398-420.
    Statistics constitute the social universe of which they are gathered. The foundation necessary to develop quantified knowledge about society is the population. If quantified knowledge changes society, the question arises on how individuals become to be represented as population. The population has to be extracted from individuals in a process that we call “populationisation.” This encompasses the development of the individual into a segment of a population through the compilation of individual data into population data and its analysis. To describe (...)
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  • Challenges in administrative data linkage for research.Harvey Goldstein, Mauricio L. Barreto, Mahmoud Azimaee, Anders Hjern, James Boyd, Chris Dibben & Katie Harron - 2017 - Big Data and Society 4 (2).
    Linkage of population-based administrative data is a valuable tool for combining detailed individual-level information from different sources for research. While not a substitute for classical studies based on primary data collection, analyses of linked administrative data can answer questions that require large sample sizes or detailed data on hard-to-reach populations, and generate evidence with a high level of external validity and applicability for policy making. There are unique challenges in the appropriate research use of linked administrative data, for example with (...)
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