In defense of "biological interaction".

Every parent who slips around the corner and unscrews the fuse when slightly annoyed by the child’s repetitive enthusiasm over having discovered the causal connection between turning the switch and the onset of light, is practicing a lay understanding of conditional causation, causal interdependence, interaction, synergy, or whatever synonymous concept we might like to use. Everyday life is full of such encounters with the logic of multicausality, and without grasping it we would be lost. Consider for a moment how our minds work to find possible explanations when facing the awkward situation of no light, despite using the switch. Etiology in the fields of medicine and public health is not very different, given that we accept the fruitful idea of weaker causal constructs that are neither sufficient nor necessary. We have long been puzzled by the lack of attention to conditional causation, causal interdependence, interaction, and synergy in empirical epidemiologic research, despite the fact that this concept is the core of many current research questions such as the “thrifty phenotype” hypothesis. We also find it puzzling that the concept remains so controversial, given the promising potential of insights into basic causal mechanisms that might guide our preventive actions. Tools such as conditional counterfactuals, potential-outcomes models, directed acyclic graphs, and the sufficient-component-cause model are currently influencing our thinking about the process of causal inference. These help us to understand what empirical evidence is required for legitimate inference of causal-effect relationships, as well as what types of inferences can be drawn. Consequently, we are also reminded to use causaleffect parameters when we report our results that have an interpretation in relation to a causal model for correct inferences to be made. Although the picture is still not completely coherent, we are not as pessimistic or even nihilistic as Lawlor, based on our understanding of recent and earlier attempts to conceptualize and analyze the issue of interaction, conditional causation, and causal interdependence from the perspective of various abstract causal models. Every discussion should start with a shared and correct understanding of the issue at stake. Interaction in its causal sense is a formal representation of the real-world example above, and implies that there is at least one mechanism in which the causal action of A (the light switch) is conditional on the presence of B (the fuse). In essence, the causal inference we are aiming at is a qualitative statement of yes or no. It has nothing to do with the choice of scale or the actual quantitative relation between the 2 causes and the outcome, although the quantitative consequences of the interaction—once established— are important, for risk evaluation and public health interpretation. Abstract causal frameworks can help us establish the nature of the empirical information needed to make sound causal inference. A beautiful example is the possibility of deriving a criterion from the sufficient-component cause and counterfactual frameworks by which we may infer from the quantitative effects found in the epidemiologic analyses whether there is