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An Axiomatic Approach to Comparing Sensitivity Parameters

Paul Diegert, Matthew A. Masten, Alexandre Poirier

arXiv 29 Apr 2025 · Econometrics

arXiv:2504.21106 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Many methods are available for assessing the importance of omitted variables. These methods typically make different, non-falsifiable assumptions. Hence the data alone cannot tell us which method is most appropriate. Since it is unreasonable to expect results to be robust against all possible robustness checks, researchers often use methods deemed "interpretable", a subjective criterion with no formal definition. In contrast, we develop the first formal, axiomatic framework for comparing and selecting among these methods. Our framework is analogous to the standard approach for comparing estimators based on their sampling distributions. We propose that sensitivity parameters be selected based on their covariate sampling distributions, a design distribution of parameter values induced by an assumption on how covariates are assigned to be observed or unobserved. Using this idea, we define a new concept of parameter consistency, and argue that a reasonable sensitivity parameter should be consistent. We prove that the literature's most popular approach is inconsistent, while several alternatives are consistent.

Citation extraction

23
references
75
in-text mentions
23
distinct cited
2
self-citations
13,384
main-text words

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Altonji, J. G., T. E. Elder, and C. R. Taber (2005) Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools1.000164100%
2Diegert, P., M. A. Masten, and A. Poirier (2025) Assessing omitted variable bias when the controls are endogenous self1.00084100%
3Cinelli, C. and C. Hazlett (2020) Making sense of sensitivity: Extending omitted variable bias1.00083100%
4Altonji, J. G., T. Conley, T. E. Elder, and C. R. Taber (2019) Methods for using selection on observed variables to address selection on unobserved variables1.00073100%
5Oster, E (2019) Unobservable selection and coefficient stability: Theory and evidence0.874122100%
6Bazzi, S., M. Fiszbein, and M. Gebresilasse (2020) Frontier culture: The roots and persistence of “rugged individualism” in the United States0.73732100%
7Masten, M. A. and A. Poirier (2025) The effect of omitted variables on the sign of regression coefficients self0.73732100%
8Abadie, A., S. Athey, G. W. Imbens, and J. M. Wooldridge (2020) Sampling-based versus design-based uncertainty in regression analysis0.64422100%
9Krauth, B (2016) Bounding a linear causal effect using relative correlation restrictions0.64422100%
10Aizer, A., N. Early, S. Eli, G. Imbens, K. Lee, A. Lleras-Muney, and… (2024) The Lifetime Impacts of the New Deal's Youth Employment Program0.40511100%

Showing the top 10 of 23 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Assessing Omitted Variable Bias when the Controls are Endogenous1.00093