Causal inference based on a restricted version of the potential outcomes approach reasoning is assuming an increasingly prominent place in the teaching and practice of epidemiology. The proposed concepts and methods are useful for particular problems, but it would be of concern if the theory and practice of the complete field of epidemiology were to become restricted to this single approach to causal inference. Our concerns are that this theory restricts the questions that epidemiologists may ask and the study designs that they may consider. It also restricts the evidence that may be considered acceptable to assess causality, and thereby the evidence that may be considered acceptable for scientific and public health decision making. These restrictions are based on a particular conceptual framework for thinking about causality. In Section 1, we describe the characteristics of the restricted potential outcomes approach (RPOA) and show that there is a methodological movement which advocates these principles, not just for solving particular problems, but as ideals for which epidemiology as a whole should strive. In Section 2, we seek to show that the limitation of epidemiology to one particular view of the nature of causality is problematic. In Section 3, we argue that the RPOA is also problematic with regard to the assessment of causality. We argue that it threatens to restrict study design choice, to wrongly discredit the results of types of observational studies that have been very useful in the past and to damage the teaching of epidemiological reasoning. Finally, in Section 4 we set out what we regard as a more reasonable ‘working hypothesis’ as to the nature of causality and its assessment: pragmatic pluralism.

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This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie their hypotheses. I argue that the mechanistic stance is indeed a bad methodology for causal inference. However, I detach and defend a mechanistic interpretation of causal generalisations in epidemiology as existence claims about underlying mechanisms. Causal hypotheses in epidemiologyWhat does it take to establish a causal hypothesis in epidemiology? What standards need to be met? Or, if establishment comes in degrees, degrees of what?The most obvious aspect of this problem concerns inferring causation in a particular study. A study reveals a statistical association between smoking and lung cancer, or a certain gene and obesity. Statistical analysis reveals a low p-value -a measure of the chance that the association is due to chance. Study design controls for confounding variables (what philosophers would call common causes of the putative cause and effect).Can it be inferred that, for this group, a causal relationship exists between smoking and lung cancer, or having that gene and obesity?Oddly enough, this is not a question that epidemiologists like to answer. A single study would not normally be considered a sufficient basis for a causal inference. Replication is a guiding epidemiological principle. From a methodological point of view this is extremely interesting. Epidemiologists' credence in a causal hypothesis about Study Group A increases when the effect is replicated in Study Group B. Explaining (or, I suppose, refuting) this attitude is a central task for any methodological analysis. 2A second difficulty concerns the inference from a study, or a collection of studies, to a wider population. Epidemiologists are centrally concerned with extrapolating from the people they study to people they have not studied. Replication is important here too, because one way to argue that differences between the population studied and the target population are causally irrelevant is to replicate the study among people who are drawn from the target population. However, replication cannot solve the problem of generalisation. Often the study group will already be drawn from the target population:for example, when generalising from the Whitehall studies to the population of Britain. 1 Differences between those studied and those not studied will always remain; the difficulty is working out when these differences make a difference. On other occasions, studies on a subset of the target population may be impractical: for an obvious example, consider future populations. Q...

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