Assessing External Validity Over Worst-case Subpopulations
This addresses the issue of brittle findings in randomized and observational studies for researchers and policymakers, though it is incremental as it builds on existing semiparametric methods.
The paper tackles the problem of assessing external validity of treatment effect studies, particularly for underrepresented subpopulations, by proposing a worst-case treatment effect (WTE) estimator that guarantees findings remain valid across subpopulations, with results showing it is a regular root-n estimator even with slow-converging nuisance parameters.
Study populations are typically sampled from limited points in space and time, and marginalized groups are underrepresented. To assess the external validity of randomized and observational studies, we propose and evaluate the worst-case treatment effect (WTE) across all subpopulations of a given size, which guarantees positive findings remain valid over subpopulations. We develop a semiparametrically efficient estimator for the WTE that analyzes the external validity of the augmented inverse propensity weighted estimator for the average treatment effect. Our cross-fitting procedure leverages flexible nonparametric and machine learning-based estimates of nuisance parameters and is a regular root-$n$ estimator even when nuisance estimates converge more slowly. On real examples where external validity is of core concern, our proposed framework guards against brittle findings that are invalidated by unanticipated population shifts.