arXiv 8 Feb 2021 · Mathematics — Statistics Theory · publishedJournal of the American Statistical Association (2022) · 32 citations (OpenAlex)
arXiv:2102.04543 · PDF · DOI · OpenAlex · Extracted main text
Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from observational data. However, its correctness relies on the untestable (and frequently implausible) assumption that all confounders have been measured. This paper introduces a robust sensitivity analysis for IPW that estimates the range of treatment effects compatible with a given amount of unobserved confounding. The estimated range converges to the narrowest possible interval (under the given assumptions) that must contain the true treatment effect. Our proposal is a refinement of the influential sensitivity analysis by Zhao, Small, and Bhattacharya (2019), which we show gives bounds that are too wide even asymptotically. This analysis is based on new partial identification results for Tan (2006)'s marginal sensitivity model.
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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 | Tan, Z (2006) A distributional approach for causal inference using propensity scores | 1.000 | 10 | 4 | 100% |
| 2 | Zhao, Q., D. S. Small, and B. B. Bhattacharya (2019) Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap | 0.941 | 12 | 7 | 83% |
| 3 | Athey, S., J. Tibshirani, and S. Wager (2019, 04) (2019) Generalized random forests | 0.928 | 5 | 3 | 80% |
| 4 | Kallus, N. and A. Zhou (2020) Minimax-optimal policy learning under unobserved confounding | 0.928 | 4 | 4 | 100% |
| 5 | Kallus, N. and A. Zhou (2018) Confounding-robust policy improvement | 0.928 | 4 | 3 | 100% |
| 6 | Koenker, R. W. and G. Bassett (1978) Regression quantiles | 0.843 | 4 | 3 | 75% |
| 7 | Lee, K., F. J. Bargagli-Stoffi, and F. Dominici (2020) Causal rule ensemble: Interpretable inference of heterogeneous treatment effects | 0.843 | 3 | 3 | 100% |
| 8 | Kallus, N., X. Mao, and A. Zhou (2019) Interval estimation of individual-level causal effects under unobserved confounding | 0.843 | 3 | 3 | 100% |
| 9 | Kallus, N. and A. Zhou (2020) Confounding-robust policy evaluation in infinite-horizon reinforcement learning | 0.843 | 3 | 3 | 100% |
| 10 | Cinelli, C. and C. Hazlett (2020) Making sense of sensitivity: Extending omitted variables bias | 0.737 | 3 | 2 | 100% |
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