Sookyo Jeong, Hongseok Namkoong
arXiv 5 Jul 2020 · Statistics — Machine Learning · 2 citations (OpenAlex)
arXiv:2007.02411 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W.… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 8 | 4 | 100% |
| 2 | A. W. van der Vaart and J. A. Wellner (1996) Weak Convergence and Empirical Processes: With Applications to Statistics | 0.693 | 9 | 3 | 33% |
| 3 | M. R. Rosenzweig and C. Udry (2020) External validity in a stochastic world: Evidence from low-income countries | 0.585 | 3 | 1 | 100% |
| 4 | E. A. Stuart, S. R. Cole, C. P. Bradshaw, and P. J. Leaf (2011) The use of propensity scores to assess the generalizability of results from randomized trials | 0.585 | 3 | 1 | 100% |
| 5 | E. Tipton (2013) Improving generalizations from experiments using propensity score subclassification: Assumptions, properties, and contexts | 0.585 | 3 | 1 | 100% |
| 6 | P. J. Bickel, C. A. Klaassen, Y. Ritov, and J. A. Wellner (1993) Efficient and adaptive estimation for semiparametric models, volume 4 | 0.511 | 5 | 2 | 20% |
| 7 | A. W. van der Vaart (1998) Asymptotic Statistics | 0.511 | 5 | 2 | 20% |
| 8 | R. T. Rockafellar and S. Uryasev (2000) Optimization of conditional value-at-risk | 0.511 | 2 | 2 | 50% |
| 9 | I. Andrews and E. Oster (2017) Weighting for external validity | 0.511 | 2 | 1 | 100% |
| 10 | J. Angrist and I. Fernandez-Val (2010) Extrapolate-ing: External validity and overidentification in the late framework | 0.511 | 2 | 1 | 100% |
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