Jacob Dorn, Kevin Guo, Nathan Kallus
arXiv 21 Dec 2021 · Statistics — Methodology · publishedJournal of the American Statistical Association (2024) · 4 citations (OpenAlex)
arXiv:2112.11449 · PDF · DOI · OpenAlex · Extracted main text
We consider the problem of constructing bounds on the average treatment effect (ATE) when unmeasured confounders exist but have bounded influence. Specifically, we assume that omitted confounders could not change the odds of treatment for any unit by more than a fixed factor. We derive the sharp partial identification bounds implied by this assumption by leveraging distributionally robust optimization, and we propose estimators of these bounds with several novel robustness properties. The first is double sharpness: our estimators consistently estimate the sharp ATE bounds when one of two nuisance parameters is misspecified and achieve semiparametric efficiency when all nuisance parameters are suitably consistent. The second is double validity: even when most nuisance parameters are misspecified, our estimators still provide valid but possibly conservative bounds for the ATE and our Wald confidence intervals remain valid even when our estimators are not asymptotically normal. As a result, our estimators provide a highly credible method for sensitivity analysis of causal inferences.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Zhao, Q., D. S. Small, and B. B. Bhattacharya (2019) Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap | 1.000 | 9 | 3 | 100% |
| 2 | Tan, Z (2006) A distributional approach for causal inference using propensity scores | 1.000 | 6 | 4 | 100% |
| 3 | Rockafellar, R. T. and S. Uryasev (2000) Optimization of conditional value-at-risk | 0.928 | 5 | 3 | 80% |
| 4 | Athey, S., J. Tibshirani, and S. Wager (2019, 04) (2019) Generalized random forests | 0.928 | 4 | 3 | 100% |
| 5 | Dorn, J. and K. Guo (2022) Sharp sensitivity analysis for inverse propensity weighting via quantile balancing self | 0.909 | 12 | 6 | 75% |
| 6 | Yadlowsky, S., H. Namkoong, S. Basu, J. C. Duchi, and L. Tian (2022) Bounds on the conditional and average treatment effect with unobserved confounding factors | 0.843 | 4 | 3 | 75% |
| 7 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.811 | 4 | 2 | 100% |
| 8 | Jin, Y., Z. Ren, and E. J. Candès (2021) Sensitivity analysis of individual treatment effects: A robust conformal inference approach | 0.811 | 4 | 2 | 100% |
| 9 | Belloni, A. and V. Chernozhukov (2011) $_1$-penalized quantile regression in high-dimensional sparse models | 0.737 | 3 | 2 | 100% |
| 10 | Soriano, D., E. Ben-Michael, P. J. Bickel, A. Feller, and S. D. Pime… (2021) Interpretable sensitivity analysis for balancing weights | 0.737 | 3 | 2 | 100% |
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