arXiv 18 Jun 2023 · Econometrics
arXiv:2306.10562 · PDF · DOI · OpenAlex · Extracted main text
Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking after residualizing the unobserved confounder on the set of observed covariates. In this paper, I explain the rationale and details of this procedure. I clarify some important details of the process of formal covariate benchmarking and highlight some of the difficulties of interpretation that researchers face in reasoning about the residualized part of unobserved confounders. I explain all the points with several empirical examples.
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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 | Cinelli, C. and Hazlett, C (2020) Making Sense of Sensitivity: Extending Omitted Variable Bias | 0.991 | 39 | 8 | 97% |
| 2 | Greene, W. H (2012) Econometric Analysis | 0.941 | 6 | 3 | 83% |
| 3 | Oster, E (2019) Unobservable Selection and Coefficient Stability | 0.874 | 7 | 2 | 100% |
| 4 | Krauth, B (2016) Bounding a Linear Causal Effect Using Relative Correlation Restrictions | 0.644 | 4 | 1 | 100% |
| 5 | VanderWeele, T. J. and Ding, P (2017) Sensitivity analysis in observational research: introducing the e-value | 0.644 | 4 | 1 | 100% |
| 6 | Kruskal, W. and Majors, R (1989) Concepts of Relative Importance in Recent Scientific Literature | 0.644 | 2 | 2 | 100% |
| 7 | Ding, P. and VanderWeele, T. J (2016) Sensitivity Analysis Without Assumptions | 0.585 | 3 | 1 | 100% |
| 8 | Rao, C. R., Toutenburg, H., Shalabh, and Heumann, C (2008) Linear Models and Generalizations: Least Squares and Alternatives | 0.511 | 2 | 2 | 50% |
| 9 | De Luca, G., Magnus, J. R., and Peracchi, F (2019) Unobservable Selection and Coefficient Stability | 0.511 | 2 | 1 | 100% |
| 10 | Angrist, J. D. and Lavy, V (1999) Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.