The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
The Phantom Menace: Omitted Variable Bias in Econometric Research
Abstract
Quantitative political science is awash in control variables. The justification for these bloated specifications is usually the fear of omitted variable bias. A key underlying assumption is that the danger posed by omitted variable bias can be ameliorated by the inclusion of relevant control variables. Unfortunately, as this article demonstrates, there is nothing in the mathematics of regression analysis that supports this conclusion. The inclusion of additional control variables may increase or decrease the bias, and we cannot know for sure which is the case in any particular situation. A brief discussion of alternative strategies for achieving experimental control follows the main result.
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“Yet, determining which combination(s) of variables to blind is non-trivial and the risk of omitted variable bias should not be downplayed [71,160].”
From Fairness in machine learning: A survey (2024) · cites this paper as [71]
In the citing paper
“Yet, determining which combination(s) of variables to blind is non-trivial and the risk of omitted variable bias should not be downplayed [71,160].”
From Fairness in machine learning: A survey (2024) · cites this paper as [71]
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“The omitted variable bias is (Clarke, 2005): 𝛼𝛼2� − 𝛼𝛼2 = 𝛼𝛼3 ∗ 𝑐𝑐𝑐𝑐𝑒𝑒𝑒𝑒(𝐶𝐶ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡,𝑂𝑂𝑂𝑂ℎ𝑒𝑒𝑒𝑒𝑡𝑡)� 𝑣𝑣𝑖𝑖𝑒𝑒(𝑂𝑂𝑂𝑂ℎ𝑒𝑒𝑒𝑒) 𝑣𝑣𝑖𝑖𝑒𝑒(𝐶𝐶ℎ𝑖𝑖𝑖𝑖𝑖𝑖) (2) where 𝑐𝑐𝑐𝑐𝑒𝑒𝑒𝑒 indicates correlation and 𝑣𝑣𝑖𝑖𝑒𝑒 variance.”
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“This term explains the way coefficients change in a regression model when excluding—or including—other independent variables in the model (22).”
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“For a systematic analysis of omitted variable bias, see, for example, Clarke (2005, 2009).”
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Did Bill C-24 Affect Voter Turnout? Evidence from the 2000 and 2004 Elections
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Decision-making in the council of the European Union. The role of committees.
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Chen, D. L., & Yeh, S. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2977074 · Cited by 8
The Queens’ gambit: Women leadership, gender expectations, and interstate conflict
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“To keep our statistical models simple, we chose to focus on the explanatory factors that consistently appear in the conflict studies scholarship (Achen, 2005; Clarke, 2005).”
Hellström, J. (2011) · Methodological Innovations Online · doi:10.4256/mio.2010.0036 · Cited by 6
“The solution can rather be achieved through a careful research design, that is by thinking carefully about which variables and cases should be included in the analysis and thus making the tests more narrow, focused and controlled (Achen, 2005a; Clarke, 2005).”
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“A related concern discussed frequently in these fields is omitted variable bias, in which misspecification of the regression problem results in biased estimates of the true regression parameters (Clarke 2005; Wooldridge 2013).”
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“This is known as the omitted variable bias problem (Clarke 2005).”
BackgroundCausal Explanation and Multi-Method Research in the Social SciencesPreprint
Kuehn, D., & Rohlfing, I. (2009) · SSRN Electronic Journal · Cited by 6
Metadata
- Authors
- Clarke, K. A.
- Affiliations
- University of Rochester (US)
- Corresponding author
- Clarke, Kevin A.
- ORCID
- Clarke, Kevin A.
- Venue
- Conflict Management and Peace Science
- Publisher
- SAGE Publications
- Published
- 2005-09
- Volume
- 22
- Issue
- 4
- Pages
- 341-352
- ISSN
- 0738-8942 (ISSN-L)
- 1549-9219
- OpenAlex
- W2146807455
- Cited by
- 484
- References
- 42
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 23 · 2023: 24 · 2022: 33 · 2021: 28 · 2020: 58 · 2019: 23 · 2018: 30 · 2017: 26 · 2016: 32 · 2015: 25 · 2014: 26 · 2013: 24 · 2012: 32
- FWCI
- 11.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Conflict Management and Peace Science (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Political Science and International Relations › Political Science Research and Education
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- omitted variable bias, control variables, regression analysis, specification bias, experimental control, econometric methodology, political science research
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