Causal Attribution, Counterfactuals, and Disease Interventions
James Woodward
Abstract
This chapter explores a number of interrelated issues that affect assessment of the global burden of disease including what can be learned from causal attributions of particular episodes of death and disability to specific diseases (such as cancer and stroke) about the effects of interventions to remove or reduce the incidence of these diseases (disease interventions). It also explores the use of counterfactuals in epidemiological causal reasoning—a methodology that is employed in the Global Burden of Disease project. It is argued that the effects of such disease interventions can be reliably predicted from causal attribution data alone only when strong additional “independence” assumptions are satisfied. It also argued that in many realistic circumstances these assumptions are unlikely to hold and that when they do not, additional information besides that provided by causal attribution data is needed to predict the effects of disease interventions. Among other things, one needs to explicitly model the causal relationships among different diseases or causes of death. This, in turn, requires frameworks (e.g., structural equations and directed graphs) that explicitly incorporate counterfactual information about what would happen if one were to intervene in various ways. Later sections discuss the sorts of variables that can or should figure in causal claims in epidemiology and the relevance of the epidemiological notions of excess and etiological fractions to predicting the results of disease interventions.