This document discusses causality in epidemiology. It defines causality as involving evidence of variations or changes, rather than just regularities. Epidemiology aims to establish variational rather than regularity claims by studying how disease variability is influenced by exposure variability. Methodologies in epidemiology like observational studies measure and test variations to establish causal relationships.

1. Causality in Epidemiology definition – evidence – rationale Federica Russo Philosophy, Louvain & Kent

2. Overview Causality Definition Evidence Rationale Motivation Regularity claims in epidemiology? Variational claims in epidemiology Goals Definitions Methodology

3. Causality: definition What is causality/cause/causal factor? By means of its properties By means of its function R-W claim in the health sciences, interpret causality as the agent’s ultimate belief (epistemic theory)

4. Causality: evidence What supports a causal claim? Probabilities Mechanisms Regular behaviour Lawlikeness … R-W claim in the health sciences, support causal claims by probabilistic and mechanistic evidence

5. Causality: rationale What notion guides causal reasoning? Rationale: principle or notion underlying some opinion, action, hypothesis, model, and the like. Rationale of causality: the notion that guides causal reasoning (e.g., model building and model testing) Russo (coming soon!): in the social sciences, causal models are regimented by a rationale of variation

6. Variational claims in epidemiology goals definitions methodology

7. Motivation Does epidemiology aim to establish regularity claims? Regularity views (rings a bell?) “ a cause is an object followed by another, and where all objects similar to the first are followed by objects similar to the second” A double reading Metaphysical: causality is regularity Epistemological: we infer causality because we observe regularities

8. Goals of epidemiology To study the variability of disease due to variability of exposure Reference to regular behaviour is absent Variation guides causal reasoning

9. Bohopol (1999) Am.J. Public Health 89(9) Certain beliefs—that epidemiology is about the study of health and disease in populations, that there is a population group variation in disease that is worth of scientific study, and that such variation is important to public health policy and practice—were common to virtually all textbooks.

10. Jewell 2004, Statistics for epidemiology In this book we describe the collection of data that speak to relationships between the occurrence of disease and various descriptive characteristics in individuals in a population. Specifically, we want to understand whether and how differences in individuals might explain patterns of disease distribution across a population.

11. Susser 1973 Causal thinking in the health sciences Epidemiologists in search for causes want to make asymmetrical statements that have direction. They seek to establish that an independent variable X causes changes in the dependent variable Y and not the reverse. The central problem of cohort studies is to cope with the change that occurs with the passage of time. The study of cause involves the detection of change in a dependent variable by change in an independent variable .

12. Lilienfeld and Stolley 1994 Foundations of epidemiology A relationship is considered causal whenever evidence indicates that the factors form part of the complex circumstances which increase the probability of occurrence of disease and that a diminution of one or more of these factors decreases the frequency of disease.

13. Definitions of cause in epidemiology Varieties of accounts, no consensus. Yet, slight preference for probabilistic characterisations Often, definitions of causal factor rather than causality Invariably, causal factors ‘ make things change’ rather than ‘occur regularly’

14. Lagiou et al (2005) Eu.J. Epidemiology 20 A factor is a cause of a certain disease when alterations in the frequency or intensity of this factor , without concomitant alterations in any other factors, are followed by changes in the frequency of occurrence of the disease , after the passage of a certain time period.

15. Susser (1973) Causal thinking in the health sciences A determinant [of health] can be any factor, whether an event, characteristic, or other definable entity so long as it brings about change for better or for worse in a health condition.

16. Karhausen (2000) Medicine, Health Care and Philosophy 3 Being a cause is a special characterisation of some special state of affairs characterised by change , i.e. an event, fact, a state, or a deed: in medicine and epidemiology a cause makes a disease happen or not happen.

17. Definitions of cause in epidemiology Is there something beside/ prior to regularity? Causality primarily involves variation and then regularity … ?

18. Methodology The claim: Methods in epidemiology measure and test variations to establish causal claims Observational studies (cohort, case-control, prospective, retrospective) Odds and risks Regression models

19. Why is ‘variation’ really relevant for methodology? Methodology should mirror one’s epistemology and metaphysics Why? Take both into account and you’ll have a better grip on causation Methodology ‘operationalise’ one’s epistemology and metaphysics What do we have to observe? (meaningful) variations What do we try to establish? Which variations are causal, by imposing constraints (invariance/stability, regularity, …)

20. To sum up and conclude Distinguish: (1) definition (2) evidence (3) rationale R-W claim in the health sciences is about (1) and (2) Variational claims in epidemiology is about (1) and (3) ‘ Variation’ sheds light on epistemological and metaphysical issues

21. Selected references Bohopol R. (1999), “Paradigms in epidemiology textbooks: in the footsteps of Thomas Khun”, Am. J. Public Health, 89(8), 1162-1165. Jewell N.P. (2004), Statistics for epidemiology , Chapman&Hall/CRC. Karhausen L.R. (2000), “Causation: the elusive grail of epidemiology”, Medicine, Health Care and Philosophy , 3, 59-67. Lagiou P., Adami H-O., Trichopoulos D. (2005), “Causality in cancer epidemiology”, Eu. J. Epidemiology , 20, 565-574. Lilienfeld D.E. & Stolley P.D. (1994), Foundations of epidemiology , Oxford University Press, 3 rd edition. Russo F. (2008). Causality and causal modelling in the social sciences. Measuring variations . Springer. In press. Russo F. & Williamson J. (2007), “Interpreting causality in the health sciences”, ISPS , 21(2), 157-170. Susser M. (1973), Causal thinking in the health sciences. Concepts and strategies of epidemiology , Oxford University Press.