Federica Russo

• Professor
• Professor at Utrecht University

Complexity science is a discipline which explores how complex systems behave and how we interact with them. Though it is widely implemented outside medicine, particularly in the sciences involving human behavior, but also in the natural sciences such as physics and biology, there are only a few applications within medical research. We propose that...

With the recent renewed interest in AI, the field has made substantial advancements, particularly in generative systems. Increased computational power and the availability of very large datasets has enabled systems such as ChatGPT to effectively replicate aspects of human social interactions, such as verbal communication, thus bringing about profou...

Whilst policymaking will always remain a highly political process, especially amidst crises, evidence-based pandemic management can benefit from adopting a socioecological perspective that integrates multi- and trans-disciplinary insights: from biology, biomedicine, mathematics, statistics, social and behavioural sciences, as well as the perspectiv...

Complexity theory is a discipline which explores how complex systems behave and how we interact with them. Though it is widely implemented outside medicine, particularly in the sciences involving human behavior, but also in the natural sciences such as physics and biology, there are only a few applications within medical research. We propose that c...

Causation is one of the most controversial topics in philosophy. There is a wide range of philosophical accounts of causation, for example, the regularity account, the probabilistic account, the counterfactual account, the interventionist account, which can be all classified as ‘difference-making’ accounts; and the mechanistic account. Many argue t...

In the science and values literature, scholars have shown how science is influenced and shaped by values, often in opposition to the ‘value free’ ideal of science. In this paper, we aim to contribute to the science and values literature by showing that the relation between science and values flows not only from values into scientific practice, but...

Participatory and collaborative approaches in sustainability science and public health research contribute to co-producing evidence that can support interventions by involving diverse societal actors that range from individual citizens to entire communities. However, existing philosophical accounts of evidence are not adequate to deal with the kind...

The health sciences have seen an unprecedented development since the ‘molecular turn’: we are now able to obtain very fine-grained information about health and disease, by identifying and validating bio-markers at different ‘omic’ levels. In this paper, I argue for the need and suitability of a complementary way to study health and disease, notably...

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychological, economic, and other processes that may be non-linear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and exp...

The Oxford Handbook of Philosophy of Political Science contains twenty-seven freshly written chapters to give the reader a panoramic introduction to philosophical issues in the practice of political science. Simultaneously, it advances the field of Philosophy of Political Science by creating a fruitful meeting place where both philosophers and prac...

This paper proposes a novel framework for the development of interventions in vulnerable populations. The framework combines a complex systems lens with syndemic theory. Whereas funding bodies, research organizations and reporting guidelines tend to encourage intervention research that (i) focuses on singular and predefined health outcomes, (ii) se...

Since antiquity, philosophers in the Western tradition of virtue ethics have declared practical wisdom to be the central virtue of citizens involved in public and social life. Practical wisdom is of particular importance when values are conflicting, power is unequal and knowledge uncertain. We propose that practical wisdom and virtue ethics can inf...

Public health, just as any policy-related field, faces the evergreen problem of turning knowledge into action. Among other problems, there is a clash between the inherent complexity of public health problems and the inevitable push, by decision-makers and the public, to simplify them. The Covid-19 pandemic has shown the insufficiencies of our curre...

The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnect...

This article addresses the contributions of the literature on the new mechanistic philosophy of science for the scientific practice of model building in ecology. This is reflected in a one-to-one interdisciplinary collaboration between an ecologist and a philosopher of science during science-in-the-making. We argue that the identification, reconstr...

In the middle the COVID-19 pandemic, we reflect ever more deeply about the nature of health and disease, and about how to design and implement effective public health interventions. For numerous diseases and conditions, as well as for COVID-19, our knowledge base is rich. We know a lot about the biology of the disease, and we have plenty of statist...

In this chapter, public health (PH) is considered as a diverse field in which a number of disciplines and approaches contribute to establishing a knowledge base for the design and implementation of interventions. I aim to explain why causality is central, yet not always explicit, in both the generation of the knowledge base and in the design proces...

The term “scientism” is used in several ways. It is used to denote an epistemological thesis according to which science is the source of our knowledge about the world and ourselves. Relatedly, it is used to denote a methodological thesis according to which the methods of science are superior to the methods of non-scientific fields or areas of inqui...

In scholarly debates, as well as in everyday parlance, we tend to pull science and technology apart: science gives us theory, and technology applies it. In practice, however, science and technologies are highly intertwined. In Techno-Scientific Practices: An Informational Approach, Federica Russo looks at the practice of science and elucidates the...

In scholarly debates, as well as in everyday parlance, we tend to pull science and technology apart: science gives us theory, and technology applies it. In practice, however, science and technologies are highly intertwined. In Techno-Scientific Practices: An Informational Approach, Federica Russo looks at the practice of science and elucidates the...

In scholarly debates, as well as in everyday parlance, we tend to pull science and technology apart: science gives us theory, and technology applies it. In practice, however, science and technologies are highly intertwined. In Techno-Scientific Practices: An Informational Approach, Federica Russo looks at the practice of science and elucidates the...

In scholarly debates, as well as in everyday parlance, we tend to pull science and technology apart: science gives us theory, and technology applies it. In practice, however, science and technologies are highly intertwined. In Techno-Scientific Practices: An Informational Approach, Federica Russo looks at the practice of science and elucidates the...

In scholarly debates, as well as in everyday parlance, we tend to pull science and technology apart: science gives us theory, and technology applies it. In practice, however, science and technologies are highly intertwined. In Techno-Scientific Practices: An Informational Approach, Federica Russo looks at the practice of science and elucidates the...

In scholarly debates, as well as in everyday parlance, we tend to pull science and technology apart: science gives us theory, and technology applies it. In practice, however, science and technologies are highly intertwined. In Techno-Scientific Practices: An Informational Approach, Federica Russo looks at the practice of science and elucidates the...

In scholarly debates, as well as in everyday parlance, we tend to pull science and technology apart: science gives us theory, and technology applies it. In practice, however, science and technologies are highly intertwined. In Techno-Scientific Practices: An Informational Approach, Federica Russo looks at the practice of science and elucidates the...

The experimental revolution in the social sciences is one of the most significant methodological shifts undergone by the field since the ‘quantitative revolution’ in the 19th century. One of the often valued features of social science experimentation is precisely the fact that there are (alleged) clear methodological rules regarding hypothesis test...

This article deals with the role of time in causal models in the social sciences. The aim is to underline the importance of time-sensitive causal models, in contrast to time-free models. The relation between time and causality is important, though a complex one, as the debates in the philosophy of science show. In particular, an outstanding issue i...

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...

In this paper, I explain how concepts, methods, and values are entangled. While the argument can be applied widely across the sciences, I focus here on the sciences of health and disease, and on public health. I will argue, on top of well-established arguments, not only that scientific methods and concepts are value-laden, but also value-promoting,...

The term ‘scientism’ has not attracted consensus about its meaning or about its scope of application. In this paper, we consider Mizrahi’s suggestion to distinguish ‘Strong’ and ‘Weak’ scientism, and the consequences this distinction may have for philosophical methodology. While we side with Mizrahi that his definitions help advance the debate, by...

Ambient particulate matter is the environmental factor with the highest contribution to global disease burden and mortality. Open questions remain regarding causality at low doses and the effects of specific pollutants. Establishing causality in regards to air pollution is methodologically challenging, affecting the establishment of regulatory poli...

Epistemic diversity is the ability or possibility of producing diverse and rich epis- temic 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. W...

There is no unified theory of causality in the sciences and in philosophy. In this paper, we focus on a particular framework, called structural causal modelling (SCM), as one possible perspective in quantitative social science research. We explain how this methodology provides a fruitful basis for causal analysis in social research, for hypothesisi...

Health is more than the absence of disease. It is also more than a biological phenomenon. It is inherently social, psychological, cultural and historical. While this has been recognised by major health actors for decades, open questions remain as to how to build systems that reflect the complexity of health, disease and sickness, and in a context t...

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...

In Sect. 7.1, we discussed extrapolation from a study population to a target population. In this chapter, we treat particularisation from a study population to one of its members. In both cases, evidence of similarity of mechanisms plays a crucial role.

This chapter introduces how to assess evidence of mechanisms, explaining a summary protocol for use of evidence of mechanisms in assessing efficacy, then external validity (developed theoretically in Part III, with tools for implementation offered in Part II). An outline of quality assessment—of a whole body of evidence, rather than individual stud...

In this chapter, we discuss how to evaluate evidence of mechanisms. This begins with an account of how a mechanistic study provides evidence for features of specific mechanism hypotheses, laying out a three step procedure of evaluating: (1) the methods used, (2) the implementation of the methods, and (3), the stability of the results. The next step...

An important problem in causal inference in medicine involves establishing causal relationships between environmental exposures and negative health outcomes. It is typically not possible to use RCTs to solve this problem, for ethical reasons. The approach outlined in this book is compared to two other prominent approaches: the procedures of the Int...

This chapter offers a brief summary of mechanisms, as including complex-system mechanisms (a complex arrangement of entities and activities, organised in such a way as to be regularly or predictably responsible for the phenomenon to be explained) and mechanistic processes (a spatio-temporal pathway along which certain features are propagated from t...

Further considerations need to be borne in mind for evidence appraisal in areas beyond clinical medicine, such as public health. This chapter looks at how public health has treated associations and correlations. Then it examines the importance to public health of mechanisms operating at the group and individual level, concerning social interactions...

In this chapter we put forward more theoretical proposals for gathering evidence of mechanisms. Specifically, the chapter covers the identification of a number of mechanism hypotheses, formulation of review questions for search, and then how to refine and present the resulting evidence. Key issues include increased precision concerning the nature o...

If theoretical developments in evidence assessment are to prove useful, guidance on implementation is essential, and this chapter fills that need. A variety of tools are offered, which can be used either in isolation, or in the various combinations suggested. The starting point is an Is your policy really evidence-based? tool which should be very w...

Previous chapters in Part III develop accounts of how to gather and evaluate evidence of claims about mechanisms. This chapter explains how this evaluation can be combined with an evaluation of evidence for relevant correlations in order to produce an overall evaluation of a causal claim. The procedure is broken down to address efficacy, external v...

One method for causal analysis in the social sciences is structural modeling. Structural models, as used in this article, model the (causal) mechanism for a social phenomenon by recursively decomposing the multivariate distribution of the variables of interest. Often, however, one does not achieve a complete decomposition in terms of single variabl...

This book is open access under a CC BY license. This book is the first to develop explicit methods for evaluating evidence of mechanisms in the field of medicine. It explains why it can be important to make this evidence explicit, and describes how to take such evidence into account in the evidence appraisal process. In addition, it develops proced...

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...

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 phy...

Research in the health sciences has been highly successful in revealing the aetiologies of many morbidities, particularly those involving the microbiology of communicable disease. This success has helped form a narrative to be found in numerous public health documents, about interventions to reduce the burden of non-communicable diseases (e.g., obe...

This article provides a systematic and pluralistic theory of causation that fits the kind of reasoning commonly found in mixed methods research. It encompasses a variety of causal concepts, notions, approaches, and methods. Each instantiation of the theory is like a mosaic, where the image appears when the tiles are appropriately displayed. This me...

Research in the health sciences has been highly successful in revealing the aetiologies of many morbidities, particularly those involving the microbiology of communicable disease. This success has helped form a narrative to be found in numerous public health documents, about interventions to reduce the burden of non-communicable diseases (e.g., obe...

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 different traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing—notably, t...

Establishing causal relations is a core enterprise of the medical sciences. Understanding the etiology of diseases, and the treatments to reduce the burden of disease, is in fact an instantiation of the very many activities related to causal analysis and causal assessment in medical science. In medicine, correlations have a “Janus” character. On th...

Epidemiology studies the variations in health in populations, according to a number of parameters. In this field, probability and statistics are used in order to provide a quantitative description and analysis of the variations in exposure and disease, as well as of the effects of possible preventatives. Thus, one goal of epidemiology is to establi...

Social scientists use different types of model to reason about social objects and to study social phenomena. In this chapter, I provide an overview of various forms of model-based reasoning in social research, especially quantitative and qualitative. In the course of the chapter, I highlight differences with other variants of model-based reasoning,...

A definitive and authoritative guide to a vibrant and growing discipline in current philosophy, The Bloomsbury Companion to Contemporary Philosophy of Medicine presents an overview of the issues facing contemporary philosophy of medicine, the research methods required to understand them and a trajectory for the discipline’s future. Written by world...

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 hel...

Determining the variables to be controlled for is usually a major problem in the social sciences when analyzing possible causal relations. A structural modelling approach, having recourse to directed acyclic graphs, is presented here as a consistent framework for determining a coherent set of guidelines when deciding what variables should be contro...

While large quantities of data have long been used by social science researchers, for example survey questionnaires, the use of massive and heterogeneous digital data, or “big data” is more and more frequent. As theory is abandoned in the search for correlations, is this multitude of data promoting a new form of determinism? On the contrary, the hi...

Epidemiology studies the variations in health in populations, according to a number of parameters. In this field, probability and statistics are used in order to provide a quantitative description and analysis of the variations in exposure and disease, as well as of the effects of possible preventatives. Thus, one goal of epidemiology is to establi...

Establishing causal relations is a core enterprise of the medical sciences. Understanding the etiology of diseases, and the treatments to reduce the burden of disease, is in fact an instantiation of the very many activities related to causal analysis and causal assessment in medical science. In medicine, correlations have a “Janus” character. On th...

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 S...

Epidemiology studies the variations in health in populations, according to a number of parameters. In this field, probability and statistics are used in order to provide a quantitative description and analysis of the variations in exposure and disease, as well as of the effects of possible preventatives. Thus, one goal of epidemiology is to establi...

Head hits cause brain damage - but not always. Should we ban sport to protect athletes? Exposure to electromagnetic fields is strongly associated with cancer development - does that mean exposure causes cancer? Should we encourage old fashioned communication instead of mobile phones to reduce cancer rates? According to popular wisdom, the Mediterra...

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 (find...

A large part of contemporary medicine is concerned with describing and understanding the biological mechanisms involved in disease causation. Comparatively less attention has been paid to the socioeconomic and behavioral mechanisms underlying disease. This article argues for an integration of social, behavioral, and biological factors in the explan...

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...

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...

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 i...

http://discovery.ucl.ac.uk/1454718/ 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...

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 b...

Lean Six Sigma (LSS) is the leading approach in organizational diagnosis. This approach is largely based on the analysis of correlations (e.g., Multivariate Analysis (MANOVA)), which constitutes the main source of information to establish the causes of dysfunction and to indicate possible interventions to restore good functioning. In this paper, we...

This paper provides an overview of structural modeling in its close relation to explanation and causation. It stems from previous works by the authors and stresses the role and importance of the notions of invariance, recursive decomposition, exogeneity and background knowledge. It closes with some considerations about the importance of the structu...

Concurrent systems identify systems, either software, hardware or even biological systems, that are characterized by sets of independent actions that can be executed in any order or simultaneously. Computer scientists resort to a causal terminology to describe and analyse the relations between the actions in these systems. However, a thorough discu...

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...

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 fal...