[PHILOS-L] [Webinar] NMO2026#1 // February 16 // Denny BORSBOOM

[Fabrizio Calzavarini]

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Monday 16 February 2026

h16-18 CEST

(https://www.timeanddate.com/worldclock/converter.html?iso=20260216T150000&p1=tz_gmt&p2=254)

Denny Borsboom

(University of Amsterdam)

Between Correlation and Causality:

Perspectives on symptom interactions in network models of psychopathology

Join at: https://unito.webex.com/unito-en/j.php?MTID=m49ca3db2d85c4959a70f47746ef6086a

Symptom network analysis is now commonly used in psychopathology research. Network analysis techniques result in networks in which symptoms are represented as nodes, while edges represent conditional associations between these nodes. As is well known, direct causal relations between symptoms will produce nonzero conditional associations, but these associations can also be produced in other ways. In this chapter, I discuss six plausible mechanisms that could produce edges in symptom networks. First, resource competition, where the presence of a symptom depletes resources which causes another symptom to arise. Second, evidential overlap, in which judgements central to different symptoms involve a subjective assessment of the same evidence. Third, shared mechanisms, in which symptomatology involves processes that are shared among different symptoms. Fourth, consistency drives, which arise when individuals are prone to align their cognitions, affect states, and behavior. Fifth, statistical processes involved in research design and analysis (marginalization and conditioning). Sixth, the presence of unobserved common causes that affect multiple symptoms at the same time. These mechanisms are illustrated through concrete examples taken from psychopathology research. I argue that, in realistic situations, the mechanisms in question are not mutually exclusive, which preempts standard scientific approaches that pit one model against another to derive critically divergent predictions. Instead, making sense of symptom networks will require more advanced theory development and modeling.

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