Paul Diegert, Matthew A. Masten, Alexandre Poirier
arXiv 6 Jun 2022 · Econometrics · 41 citations (OpenAlex)
arXiv:2206.02303 · PDF · DOI · OpenAlex · Extracted main text
Omitted variables are one of the most important threats to the identification of causal effects. Several widely used methods assess the impact of omitted variables on empirical conclusions by comparing measures of selection on observables with measures of selection on unobservables. The recent literature has discussed various limitations of these existing methods, however. This includes a companion paper of ours which explains issues that arise when the omitted variables are endogenous, meaning that they are correlated with the included controls. In the present paper, we develop a new approach to sensitivity analysis that avoids those limitations, while still allowing researchers to calibrate sensitivity parameters by comparing the magnitude of selection on observables with the magnitude of selection on unobservables as in previous methods. We illustrate our results in an empirical study of the effect of historical American frontier life on modern cultural beliefs. Finally, we implement these methods in the companion Stata module regsensitivity for easy use in practice.
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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 | Bazzi, S., M. Fiszbein, and M. Gebresilasse (2020) Frontier culture: The roots and persistence of “rugged individualism” in the United States | 1.000 | 24 | 3 | 100% |
| 2 | Oster, E (2019) Unobservable selection and coefficient stability: Theory and evidence | 1.000 | 11 | 3 | 100% |
| 3 | Diegert, P., M. A. Masten, and A. Poirier (2025) An axiomatic approach to comparing sensitivity parameters self | 1.000 | 9 | 3 | 100% |
| 4 | Cinelli, C. and C. Hazlett (2020) Making sense of sensitivity: Extending omitted variable bias | 0.928 | 5 | 3 | 80% |
| 5 | Altonji, J. G., T. E. Elder, and C. R. Taber (2005) Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools | 0.874 | 6 | 2 | 100% |
| 6 | Hosman, C. A., B. B. Hansen, and P. W. Holland (2010) The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder | 0.644 | 3 | 2 | 67% |
| 7 | Masten, M. A. and A. Poirier (2018) Identification of treatment effects under conditional partial independence self | 0.644 | 2 | 2 | 100% |
| 8 | Masten, M. A. and A. Poirier (2025) The effect of omitted variables on the sign of regression coefficients self | 0.585 | 3 | 1 | 100% |
| 9 | Basu, D (2022) Bounds for bias-adjusted treatment effect in linear econometric models | 0.511 | 2 | 1 | 100% |
| 10 | De Luca, G., J. R. Magnus, and F. Peracchi (2019) Unobservable selection and coefficient stability: Theory and evidence | 0.511 | 2 | 1 | 100% |
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