Estimating causal parameters without target populations

In recent years, numerous research methodologists have argued forcefully that any estimated effect from an observational study or a randomized trial should apply to a 'target population'- to a finite group of people. Some methods to adjust for confounders heavily draw upon this idea.

I cite a recently published paper in The American Journal of Epidemiology that linked methods to adjust for confounders to the concept of a 'target population'. I explain that the requirement to specify a finite population as the target of causal inference is rooted in two extreme models of causation: determinism and stochastic causation.

I argue that the 'target population' epistemology is scientifically irrelevant and so are methods to handle confounders that are founded on this paradigm, namely, standardization, inverse-probability-of-treatment weighting and SMR-weighting. Finally, I propose a simple alternative framework under an indeterministic model of causation. According to my proposed model, a causal parameter is not tied to any finite population and its estimate is a (fallible) scientific conjecture about a homogeneous, individual-level effect.