Reconstructing the mixed mechanisms of health: the role of bio- and socio-markers

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 the causal continuum from exposure to disease development at the biological level, socio-markers ought to be introduced and studied in order to trace the social continuum from exposure to disease development. We explain how socio-markers differ from social indicators and how they can be used in combination with bio-markers in order to reconstruct the mixed mechanisms of health and disease, namely mechanisms in which both biological and social factors have an active causal role

Inferring causation from big data in the social sciences

The emergence of big data has become a central theme in scientific and philosophical discussions. A main tenor in the literature is that big data can drastically change the way in which causal studies are conducted. My thesis aims to explore how big data can be used to establish causal relationships in the social sciences. The beginning of the thesis will focus on data-driven studies and will investigate some of the limitations that characterise this type of study. This analysis will lead me to identify three key challenges of big data for causal studies in the social sciences. The first challenge is how to overcome the limitations of data-driven causal studies. This challenge is motivated by the observation that, regardless of how sophisticated they are, causal data-driven methods can suffer from bias. The second challenge is how to understand the role of ethnographic, qualitative data in causal studies based on big data. This challenge appears vital in the social sciences, where some researchers remain hesitant about the use of data-driven methods and try to defend the importance of qualitative, 'thick' data. The third challenge is how to use big data, in the social sciences, to obtain evidence of causality that goes beyond correlations. This challenge is strongly associated with the idea that, in order to establish causation, both the presence of a correlation between the cause and the effect, and the presence of a mechanism linking the cause and the effect need to be established. This idea, originally proposed by Russo and Williamson (2007) and known by the name of the Russo-Williamson thesis, will be discussed in detail to provide a solution to the first challenge. I will argue that researchers should comply with such a thesis to overcome the limitations of data-driven causal studies in the social sciences. Next, I shall examine the discussions on mixed methods research to claim that qualitative ethnographic data can be used both to collect evidence of social mechanisms, and to help researchers to obtain a comprehensive understanding of the phenomenon under study. Finally, I shall argue that big data can be used, in specific circumstances, to collect evidence of entities and activities constituting causal mechanisms, and that big data might be used to identify sociomarkers, the social version of biomarkers, to trace causal processes that evolve over time

Taking the Russo-Williamson thesis seriously in the social sciences

The Russo Williamson thesis (RWT) states that a causal claim can be established only if it can be established that there is a difference-making relationship between the cause and the effect, and that there is a mechanism linking the cause and the effect that is responsible for such a difference-making relationship (Russo & Williamson, 2007). The applicability of Russo and Williamson’s idea was hugely debated in relation to biomedical research, and recently it has been applied to the social sciences (Shan & Williamson, 2021). While many philosophers and social scientists have advocated the use of different kinds of evidence for causal discoveries, others have criticised this approach. With this paper, I aim to defend RWT from criticisms and to show its importance in the social sciences. The paper is structured as follows. After a brief introduction, in Sect. 2, I will summarise RWT, and in Sect. 3 I will describe how this approach can be applied to the social sciences. In Sect. 4, I will reconstruct two main criticisms of this thesis proposed in the philosophy of the social sciences literature: namely (i) RWT is not used in the social sciences, (ii) RWT does not work. For each criticism I will provide a defence of RWT. My defence will be based on two general considerations: (i) RWT appears perfectly in line with the research methods used in the social sciences and (ii) RWT can be applied successfully to establish causal claims. In Sect. 5, moreover, I will examine the causal accounts that have motivated such criticisms and I will argue that they should be rejected to endorse RWT and a causal account able to accommodate the current use of mechanistic and difference-making evidence in the social sciences. Section 6 will conclude with a note on the relevance of RWT in both its descriptive and normative form

Reconstructing the mixed mechanisms of health: the role of bio- and socio-markers

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 the causal continuum from exposure to disease development at the biological level, socio-markers ought to be introduced and studied in order to trace the social continuum from exposure to disease development. We explain how socio-markers differ from social indicators and how they can be used in combination with bio-markers in order to reconstruct the mixed mechanisms of health and disease, namely mechanisms in which both biological and social factors have an active causal role

Disambiguating the Role of Paradigms in Mixed Methods Research

In the mixed methods research (MMR) literature, the term paradigm is used in a number of ways to support very different accounts. This article aims to contribute to the ongoing dialogue about the relationship between MMR and paradigms by analyzing two main claims discussed in the literature: (a) MMR is a new paradigm and (b) MMR mixes different paradigms. Focusing on the notion of paradigms used to support each claim, it clarifies why MMR can be considered a new paradigm and discusses conditions under which it is possible to mix two or more paradigms within a single study. This clarification promotes a more clear-cut use of concepts such as paradigms and worldviews in the literature