Matthew A. Masten, Alexandre Poirier, Muyang Ren
arXiv 26 Jan 2025 · Econometrics
arXiv:2501.15400 · PDF · DOI · OpenAlex · Extracted main text
This paper defines a general class of relaxations of the unconfoundedness assumption. This class includes several previous approaches as special cases, including the marginal sensitivity model of Tan (2006). This class therefore allows us to precisely compare and contrast these previously disparate relaxations. We use this class to derive a variety of new identification results which can be used to assess sensitivity to unconfoundedness. In particular, the prior literature focuses on average parameters, like the average treatment effect (ATE). We move beyond averages by providing sharp bounds for a large class of parameters, including both the quantile treatment effect (QTE) and the distribution of treatment effects (DTE), results which were previously unknown even for the 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 | 7 | 3 | 100% |
| 2 | Masten, M. A. and A. Poirier (2018) a): Identification of treatment effects under conditional partial independence self | 1.000 | 5 | 3 | 100% |
| 3 | Dorn, J., K. Guo, and N. Kallus (2024) Doubly-valid/doubly-sharp sensitivity analysis for causal inference with unmeasured confounding | 0.928 | 4 | 4 | 100% |
| 4 | Fan, Y. and S. S. Park (2010) Sharp bounds on the distribution of treatment effects and their statistical inference | 0.874 | 6 | 3 | 67% |
| 5 | Dorn, J. and K. Guo (2023) Sharp sensitivity analysis for inverse propensity weighting via quantile balancing | 0.843 | 3 | 3 | 100% |
| 6 | Rambachan, A., A. Coston, and E. Kennedy (2023) Robust design and evaluation of predictive algorithms under unobserved confounding | 0.737 | 3 | 2 | 100% |
| 7 | Kallus, N. and A. Zhou (2018) Confounding-robust policy improvement, in | 0.644 | 2 | 2 | 100% |
| 8 | Masten, M. A. and A. Poirier (2020) Inference on breakdown frontiers self | 0.644 | 2 | 2 | 100% |
| 9 | Zhao, Q., D. S. Small, and B. B. Bhattacharya (2019) Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap | 0.511 | 2 | 1 | 100% |
| 10 | Bonvini, M. and E. H. Kennedy (2022) Sensitivity analysis via the proportion of unmeasured confounding | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Policy Learning under Unobserved Confounding: A Robust and Efficient Approach | 0.405 | 1 | 1 |
| 2 | TITLE | 0.405 | 1 | 1 |