arXiv 31 Jul 2025 · Statistics — Methodology
arXiv:2507.23743 · PDF · DOI · OpenAlex · Extracted main text
To conduct causal inference in observational settings, researchers must rely on certain identifying assumptions. In practice, these assumptions are unlikely to hold exactly. This paper considers the bias of selection-on-observables, instrumental variables, and proximal inference estimates under violations of their identifying assumptions. We develop bias expressions for IV and proximal inference that show how violations of their respective assumptions are amplified by any unmeasured confounding in the outcome variable. We propose a set of sensitivity tools that quantify the sensitivity of different identification strategies, and an augmented bias contour plot visualizes the relationship between these strategies. We argue that the act of choosing an identification strategy implicitly expresses a belief about the degree of violations that must be present in alternative identification strategies. Even when researchers intend to conduct an IV or proximal analysis, a sensitivity analysis comparing different identification strategies can help to better understand the implications of each set of assumptions. Throughout, we compare the different approaches on a re-analysis of the impact of state surveillance on the incidence of protest in Communist Poland.
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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 | Hager, A. and Krakowski, K (2022) Does state repression spark protests? evidence from secret police surveillance in communist poland | 0.920 | 9 | 5 | 78% |
| 2 | Cinelli, C. and Hazlett, C (2020) Making sense of sensitivity: Extending omitted variable bias | 0.874 | 9 | 5 | 67% |
| 3 | Miao, W., Liu, L., Tchetgen, E. T., and Geng, Z (2015) Identification, doubly robust estimation, and semiparametric efficiency theory of nonignorable missing data with a shadow variable | 0.737 | 3 | 2 | 100% |
| 4 | Andrews, I., Stock, J. H., and Sun, L (2019) Weak instruments in instrumental variables regression: Theory and practice | 0.644 | 2 | 2 | 100% |
| 5 | Imbens, G. W (2003) Sensitivity to exogeneity assumptions in program evaluation | 0.644 | 2 | 2 | 100% |
| 6 | Miao, W., Geng, Z., and Tchetgen Tchetgen, E. J (2018) Identifying causal effects with proxy variables of an unmeasured confounder | 0.644 | 2 | 2 | 100% |
| 7 | Bound, J., Jaeger, D. A., and Baker, R. M (1995) Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory vari… | 0.405 | 1 | 1 | 100% |
| 8 | Carnegie, N. B., Harada, M., and Hill, J. L (2016) Assessing sensitivity to unmeasured confounding using a simulated potential confounder | 0.405 | 1 | 1 | 100% |
| 9 | Chernozhukov, V., Cinelli, C., Newey, W., Sharma, A., and Syrgkanis, V (2024) Long story short: Omitted variable bias in causal machine learning | 0.405 | 1 | 1 | 100% |
| 10 | Cinelli, C. and Hazlett, C (2025) An omitted variable bias framework for sensitivity analysis of instrumental variables | 0.405 | 1 | 1 | 100% |
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