Editor,— The editorial by Paolo Vineis1 is an interesting attempt to clarify various interpretations of causality in the medical literature. The following remarks are to be taken as a mere running commentary of the excellent paper by Vineis.

Causation is a conditional relation such as: if C then D. Ventricular standstill causes death: if the ventricles fails to contract, the patient dies.

What binds causes to their effect? Counterfactual conditionals (or counterfactuals) introduce some sort of necessity in a causal relation. Counterfactuals have the following structure:

Assuming that (1) If C then D and (2) C and D occur.

then we have: If C had not occurred (though it did), D would not have occurred.

Counterfactuals are conditionals that are contrary to the facts as the first part of the conditional “if C had not occurred” is false. Counterfactuals translate into predictions and introduce some support of necessity that allows to separate statements that are accidentally true from those which are regularly true: in the second case, our knowledge extends to the counterfactual cases.

Incidentally, I have serious reservations about the usefulness of Hempel's or Popper's hypothetico-deductive models because there are no such things as general laws in medicine: medical explanation is not deductive.

Vineis distinguishes five major interpretations of causality in medicine. After discussing an earlier broad Aristotelian one he brings in a second stream, springing from the success of bacteriology and according to which causality is analysed in terms of agency: a cause is a single, necessary factor of its effect. If C is a necessary cause of D, than D necessitates C in each way of maintaining other causal or interacting factors constant. A necessary factor is a sine qua non determinant on which the effect depends; it usually involves a cause-effect relation of some mixed nature, at once empirical and semantic. Necessary factors depend on the way we carve the universe, a point highlighted by MacMahon.2

According to the third stream, a cause is a sufficient (that is, necessitating) condition of its effect. If C is a sufficient condition of D, then C necessitates D in each way of holding other causal or interacting factors constant: it hinges on the degree to which C produces D in contrast with the weaker tendencies of alternative causal background conditions.

For Mackie, a cause is part of some sufficient conditions—that is, a INUS condition. Exposed attributable fraction (or Cole's attributable proportion) is the INUS element namely Mackie's necessary causal component of a sufficient set; it estimates how necessary the exposure is for the occurrence of the disease or, counterfactually, what is the probability that the disease would not have occurred among the non-exposed, given that exposure and disease actually occurred.

Yet, there is, as it were, no known example in medical science of a condition sufficient for its effect so that when this deterministic model broke down a fourth stream emerged as the ideas of sufficient and necessary causes were being traded away for a probabilistic model.3 This new account revolves around the transmission of probability distributions: probabilities or propensities replace sufficient causes as metaphysical determinism begins to fade away.

What should we now do with the concepts of sufficiency and necessity? Actually, one of the consequences of the probabilistic turn is that sufficiency and necessity in causal contexts are often not categorical but apt to come by degree.4 5 Thus the question raised by cohort studies is the following one: to what extent is C regularly followed by D? Such studies measure the tendency towards causal sufficiency of C for D or the capacity of C to produce D; or, counterfactually, the probability that unexposed persons in a healthy population would have contracted the disease had they been exposed.

Conversely, case-control studies raise a different question: to what degree can the previous occurrence of C be inferred from the occurrence of D? Such studies estimate the tendency towards necessity of the putative causal factor—that is, (counterfactually) what proportion of the diseased population would not have caught the disease, had they not been exposed, given that exposure and disease actually occurred.

The fifth stream is raised by the papers by Portaet al 6 7 and the following discussion by Trichopoulos and colleagues.8 Wesley Salmon distinguishes causal processes from causal interactions.9The latter involve the production of change and are concerned, as discussed above, with causal relations between events, namely the object of epidemiological methods; they involve questions of the type: why A in contrast to B? On the other hand, causal processes bring about “how” questions; they involve the spatio-temporal continuous propagation of causal influence from one location to another, so that the basic kinds are here processes rather than events. We knew that smoking causes lung cancer long before the details of the links between the smoking and the lung cancer were traced. These two viewpoints identify two approaches in medical research with relevant distinct methodologies: epidemiology on the one hand and experimental medicine—that is, investigation of physiopathological mechanisms of disease processes on the other.

But even though epidemiology and medical science in general, are predicated upon some sort of connection between streams of facts, causes and causal power are, in Hume's view, not observable facts of nature: causation vanishes into our grounds for it. Causes are manmade conceptual artefacts that are relative to the kind of question that needs to be answered: Collingwood was writing that “the cause of an event in nature is the handle so to speak, by which we can manipulate it”.10