When researchers conduct causal studies, in general social phenomena are analysed through social indicators and proxies, which are then used to establish correlations. Some examples of this approach can be found in the case studies discussed in chapter 5 and in chapter 6. When Garip and Asad (2016) studied the phenomenon of Mexico-U.S. migration, they used indicators and proxies measuring political, socio-economic, and demographic factors to find significant correlations to individuals’ likelihood to migrate. Similarly, in LIFEPATH researchers study the social determinants of health by measuring three aspects of socio-economic position (education, occupation, and income) and using such measures as indicators. Such indicators are then used to find statistical associations with biological factors. In such a way, researchers manage to accumulate a large and compelling body of evidence demonstrating that socio-economic factors are correlated to a wide range of health outcomes. Unfortunately, however, often the use of socio- economic indicators and proxies does not help to uncover the causal mechanisms behind such correlations.
The case of LIFEPATH is particularly interesting because it is possible to compare the way in which researchers use socio-economic indicators with the way in which researchers study biomarkers. As I have argued in chapter 6, biomarkers can be used to trace the biological mechanisms leading to the formation of diseases. Socio-economic indicators, nevertheless, can only be correlated to such mechanisms. Considering this difference, it might be questioned whether, like in the case of biological data, also social data can be used as ‘picking up signals’ to trace etiological mechanisms at the social level.
8.3.1 Towards a general notion of markers
In chapter 6, I have argued that, traditionally, epidemiology established correlations between population-level variables: those associated with the environment and those describing diseases. The identification of biomarkers, instead, allows researchers to go much smaller. Not only the population-level factors themselves, such as air pollution, can be studied at the chemical level, but also their effects are now measured at the molecular
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level. By considering all these factors together, researchers can finally reconstruct the process leading to disease and establish causal links between exposure and disease. The analysis of markers at the biological level has led me to observe, in section 6.5.1, that: i) a biomarker can be any measure of a biological entity, quality or event; ii) a biomarker is objectively measurable; iii) a biomarker is linked to the causal process under study either because is directly involved in the process, or because is caused by the same causal factor A, is caused by another unmeasurable factor involved in the process or is a background condition of the causal process (as illustrated in Figure 18); iv) a biomarker helps researchers to develop a causal hypothesis or to provide evidence for a hypothesised causal process linking the cause A to the causal effect B.
I can now try to adjust these features to develop a general notion of markers, that can be applied to different domains. If we consider scientific research in general, it can be said that it is aimed at developing causal explanations of phenomena: researchers do not only aim to claim that some factors are correlated, they want to establish that some factors are
causally linked and want to explain how something can cause something else. While the
recognition that there is a causal link can be obtained through different methods; typically, the ‘how’ question is answered by considering the etiological mechanism linking the putative causal factor to the effect. To do so, researchers have to detect something that can help them to identify the link.
When the causal process appears to be particularly complex, researchers might trace it by means of some markers. These markers are clues, signals to detect in order to trace the chain linking the putative cause to the final outcome (Illari and Russo 2016). In many cases markers are not just measures of ‘objects out there’: they can be measures of actual entities, but can also be just measures of specific characteristics or events offering some insights into the causal process under study. In all these cases, when something is used as a marker, it should be possible to objectively measure it. Similarly, in order to use something as a marker, researchers should be able at least to hypothesise the reason for the correlation between the marker and the phenomenon under study: in general, such a correlation is due to one of the situations represented in Figure 18 of chapter 6. Markers, finally, are always defined by the function they have in a particular process of inquiry: nothing is a marker per se, and any measure of an entity, quality or event can become a
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marker if researchers think that it can help to develop a causal hypothesis or to provide evidence for a hypothesised causal process.
While biological markers are often used as signals to trace processes that happen at the biological level, the use of data at the social level is still based on the traditional category of direct measurements, indicators and proxies. My argument is that, even at the social level, it is possible to pick up signals of causal processes. I call these signals sociomarkers.