Federica Russo

Interpreting Causality in the Health Sciences

We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms, and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences—pluralism about causality will not do either. Instead, we maintain, the health sciences require a theory of causality that unifies its mechanistic and probabilistic aspects. We argue that the epistemic theory of causality provides the required unification.

Do we necessarily need longitudinal data to infer causal relations

A-t-on nécessairement besoin de données longitudinales pour inférer des relations causales ? Il est généralement admis que les causes précèdent leurs effets dans le temps. Cela justifie usuellement la préférence pour les études longitudinales par rapport aux études transversales, parce que les premières permettent la modèlisation du processus dynamique engendrant le résultat, tandis que les secondes ne le peuvent pas. Les partisans de l’approche longitudinale proposent deux justifications interdépendantes : (i) l’inférence causale nécessite le suivi des mêmes personnes au fil du temps, et (ii) aucune inférence causale ne peut être tirée de données transversales. Dans cet article, nous remettons en question ce point de vue et proposons des objections à ces deux arguments. Nous soutenons également que la possibilité d’établir des relations de cause à effet ne dépend pas tant de l’utilisation de données longitudinales ou transversales, mais plutôt de savoir si la stratégie de modélisation est d’ordre structurel ou non. It is generally admitted that causes precede their effects in time. This usually justifies the preference for longitudinal studies over cross-sectional ones, because the former allow the modelling of the dynamic process generating the outcome, while the latter cannot. Supporters of the longitudinal view make two interrelated claims: (i) causal inference requires following the same individuals over time, and (ii) no causal inference can be drawn from cross-sectional data. In this paper, we challenge this view and offer counter-arguments to both claims. We also argue that the possibility of establishing causal relations does not so much depend upon whether we use longitudinal or cross-sectional data, but rather on whether or not the modelling strategy is structural.

Critical data studies: An introduction:

Critical Data Studies (CDS) 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 helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles.

Structural Modelling, Exogeneity, and Causality

This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based which is based on (i) congruence with background knowledge, (ii) invariance under a large variety of environmental changes, and (iii) model fit. We also tackle the issue of confounding and show how latent confounders can play havoc with exogeneity. This framework avoids making untestable metaphysical claims about causal relations and yet remains useful for cognitive and action-oriented goals.

The Integration of Social, Behavioral, and Biological Mechanisms in Models of Pathogenesis

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 explanation of pathogenesis, a perspective that is in accord with the vision of pioneer public health practitioners of the 19th century, but that has gradually been overtaken by the dominance of the biomedical disease model. In recent decades, the social components of disease have been depicted as “distal” factors or used as “classificatory” devices. We explain how the integration we propose, which draws upon the concepts of “mixed mechanism” and of “lifeworld,” advances the view of several scholars of the recent past. Finally, we discuss new findings in epigenetics and psychology, where socioeconomic disparities appear to be an integral part of the explanation of health conditions, to illustrate how the integration may work in practice.

Inferring causality through counterfactuals in observational studies - Some epistemological issues

L’inférence causale par contrefactuels dans les études observationnelles — Quelques épistémologiques : Cet article contribue au débat sur les vertus et les vices de contrefactuels comme base pour l’inférence causale. L’objectif est de mettre l’approche contrefactuelle dans une perspective épistémologique. Nous discutons d’un certain nombre de questions, allant de sa base non observable au parallélisme établi entre cette approche en statistique et en philosophie. Nous soutenons que la question n’est pas de rejeter ou d’approuver l’approche contrefactuelle par principe, mais de décider quel cadre de modélisation est préférable en fonction du contexte de la recherche. This paper contributes to the debate on the virtues and vices of counterfactuals as a basis for causal inference. The goal is to put the counterfactual approach in an epistemological perspective. We discuss a number of issues, ranging from its non-observable basis to the parallelisms drawn between the counterfactual approach in statistics and in philosophy. We argue that the question is not to oppose or to endorse the counterfactual approach as a matter of principle, but to decide what modelling framework is best to adopt depending on the research context.

Causal narratives in public health: the difference between mechanisms of aetiology and mechanisms of prevention in non-communicable diseases

Abstract 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., obesity or alcohol related pathologies). These focus on tackling the purported pathogenic factors causing the diseases as a means of prevention. In this paper, we argue that this approach has been sub‐optimal. The mechanisms of aetiology and of prevention are sometimes significantly different and failure to make this distinction has hindered efforts at preventing non‐communicable diseases linked to diet, exercise and alcohol consumption. We propose a sociological approach as an alternative based on social practice theory. (A virtual abstract for this paper can be found at: https://www.youtube.com/channel/UC_979cmCmR9rLrKuD7z0ycA).

Causal models and evidential pluralism in econometrics

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 incorporate important causal information which contributes to their causal interpretation. The primary objective of this paper is to argue that causal claims are established on the basis of a plurality of evidence. We discuss the consequences of ‘evidential pluralism’ in the context of econometric modelling.

What Invariance Is and How to Test for It

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 whether X causes Y. This account of invariance (under intervention) has been criticised, among other reasons, because it makes manipulations on the putative causal factor fundamental for the causal methodology; consequently, the argument goes, the account is ill-suited to those contexts where manipulations are not performed, for instance, the social sciences. The article aims to extend the account of invariance (under intervention), in a way that manipulations on the putative causal factors are not methodologically fundamental, and yet invariance remains key for causal assessment both in experimental and non-experimental contexts.

Combining probability and logic

This volume arose out of an international, interdisciplinary academic network on Probabilistic Logic and Probabilistic Networks involving four of us (Haenni, Romeijn, Wheeler and Williamson), called Progicnet and funded by the Leverhulme Trust from 2006–8. Many of the papers in this volume were presented at an associated conference, the Third Workshop on Combining Probability and Logic (Progic 2007), held at the University of Kent on 5–7 September 2007. The papers in this volume concern either the special focus on the connection between probabilistic logic and probabilistic networks or the more general question of the links between probability and logic. Here we introduce probabilistic logic, probabilistic networks, current and future directions of research and also the themes of the papers that follow.