Specification Credibility
Specification Credibility asks whether the analysis is sound enough to support population-level inference. Building on theoretical foundations (Model Utility) and empirical leverage (Scope Plausibility), this criterion addresses how to analyze data credibly. We organize Specification Credibility into three components, tracking from full identifiability to partial identifiability to settings where mechanism-based reasoning is required.
Components of Specification Credibility
SC1: Effect Estimation
When identifiability is plausible, population-level effects can be estimated using covariate-based methods (subclassification, weighting, matching), model-based methods (regression, doubly-robust), or hybrid approaches—extended to non-unit STOUT dimensions and estimands like PATE[S] or TATE[Ti].
SC2: Uncertainty Quantification
When assumptions are weak or only partially met, uncertainty quantification becomes essential. Methods include alternative estimands that relax assumptions, robustness checks across specifications, and synthesis approaches that support cautious inference.
SC3: Abductive Prediction
When mechanisms or STOUT contexts differ substantially and empirical identification fails, abductive prediction supports disciplined reasoning—anchoring inferences in explicit mechanisms, M-STOUT differences, and testable predictions rather than informal speculation.
Integrating All Three Components
Analytical approaches for external validity should seek to engage all three components. Even when population-level effect estimation (SC1) is possible, researchers should also quantify uncertainty (SC2) and engage in abductive prediction (SC3) to understand the conditions under which effects might vary. When precise estimation of population-level effects is challenging—due to limited overlap, sparse data, or weak identification—researchers should prioritize SC2 and SC3. And when empirical identification fails entirely, SC3 alone may be the only viable path forward, anchoring inferences in explicit mechanisms and testable predictions rather than leaving external validity claims implicit or unexamined.