  The most common approach to causal modelling is the potential outcomes
framework due to Neyman and Rubin. In this framework, outcomes of
counterfactual treatments are assumed to be well-defined. This metaphysical
assumption is often thought to be problematic yet indispensable. The
conventional approach relies not only on counterfactuals, but also on abstract
notions of distributions and assumptions of independence that are not directly
testable. In this paper, we construe causal inference as treatment-wise
predictions for finite populations where all assumptions are testable; this
means that one can not only test predictions themselves (without any
fundamental problem), but also investigate sources of error when they fail. The
new framework highlights the model-dependence of causal claims as well as the
difference between statistical and scientific inference.
