arXiv 12 Sep 2023 · Econometrics
arXiv:2309.06305 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel sensitivity analysis framework for linear estimators with identification failures that can be viewed as seeing the wrong outcome distribution. Our approach measures the degree of identification failure through the change in measure between the observed distribution and a hypothetical target distribution that would identify the causal parameter of interest. The framework yields a sensitivity analysis that generalizes existing bounds for Average Potential Outcome (APO), Regression Discontinuity (RD), and instrumental variables (IV) exclusion failure designs. Our partial identification results extend results from the APO context to allow even unbounded likelihood ratios. Our proposed sensitivity analysis consistently estimates sharp bounds under plausible conditions and estimates valid bounds under mild conditions. We find that our method performs well in simulations even when targeting a discontinuous and nearly infinite bound.
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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 | Masten, M. A. and A. Poirier (2018) Identification of treatment effects under conditional partial independence | 1.000 | 9 | 3 | 100% |
| 2 | Tan, Z (2006) A distributional approach for causal inference using propensity scores | 1.000 | 7 | 3 | 100% |
| 3 | Frauen, D., V. Melnychuk, and S. Feuerriegel (2023) Sharp Bounds for Generalized Causal Sensitivity Analysis | 1.000 | 5 | 3 | 100% |
| 4 | Tan, Z (2022) Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation | 1.000 | 5 | 3 | 100% |
| 5 | Gerard, F., M. Rokkanen, and C. Rothe (2020) Bounds on treatment effects in regression discontinuity designs with a manipulated running variable | 0.969 | 11 | 3 | 91% |
| 6 | Dorn, J. and K. Guo (2023) Sharp sensitivity analysis for inverse propensity weighting via quantile balancing self | 0.928 | 5 | 4 | 80% |
| 7 | Dorn, J., K. Guo, and N. Kallus (2022) Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding self | 0.843 | 4 | 4 | 75% |
| 8 | Bertsimas, D., K. Imai, and M. L. Li (2022) Distributionally robust causal inference with observational data | 0.644 | 3 | 2 | 67% |
| 9 | Masten, M. A., A. Poirier, and L. Zhang (2024) Assessing Sensitivity to Unconfoundedness: Estimation and Inference | 0.644 | 2 | 2 | 100% |
| 10 | Ramsahai, R. R (2012) Causal Bounds and Observable Constraints for Non-deterministic Models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 32 scored citations.