Causal inference without counterfactuals

A popular approach to the framing and answering of causal questions relies on the idea of counterfactuals: outcomes that would have been observed had the world developed differently; for example, if the patient had received a different treatment. By definition one can never observe such quantities, nor assess empirically the validity of any modeling assumptions made about them, even though one's conclusions may be sensitive to these assumptions. Here I argue that for making inference about the likely effects of applied causes, counterfactual arguments are unnecessary and potentially misleading. An alternative approach, based on Bayesian decision analysis, is presented. Properties of counterfactuals are relevant to inference about the likely causes of observed effects, but close attention then must be given to the nature and context of the query, as well as to what conclusions can and cannot be supported empirically. In particular, even in the absence of statistical uncertainty, such inferences may be subject to an irreducible degree of ambiguity.