The Phantom Menace: Omitted Variable Bias in Econometric Research
By Kevin A. Clarke. 2005
522 citations recorded by the source corpus.
Subfields: Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Sociology and Political Science
- Kristian Lum
- James E. Johndrow
- J. Gerring
- Kevin A. Clarke
- Christopher H. Achen
- Richard York
- Aron Culotta
- Virgile Landeiro
Citations in the corpus
- A Survey on Bias and Fairness in Machine Learning (2019, background): “Omitted variable bias4 occurs when one or more important variables are left out of the model [38, 114, 131].”
- An algorithm for removing sensitive information: Application to race-independent recidivism prediction (2017, methodology): “In the case of regression models, this is known as omitted variable bias [Clarke, 2005].”
- Cross‐Country Determinants of Early‐Stage Necessity and Opportunity‐Motivated Entrepreneurship: Accounting for Model Uncertainty (2018, unknown): “The problem is that when the model is not correctly specified, controlling for some variables but not others is as likely to reduce bias as it is to increase it (Clarke 2005, 2009).”
- Robust Text Classification in the Presence of Confounding Bias (2016, background): “This may inflate the coefficients of age-related terms in the clas-sifier (a result of omitted-variable bias (Clarke 2005)).”
- Fair Kernel Learning (2017, background): “1 age c [1,5] 2 workclass d [6,13] 3 final weight c [14,18] 4 education d [19,34] 5 ed num c [35,39] 6 marital status d [40,46] 7 occupation d [47,60] 8 relationship d [61,66] 9 race d [67,71] 10 sex d [72,73] 11 capital gain c [74,75] 12 capital loss c [76,77] 13 hours× week c [78,82] 14 country d ”
- Return of the Phantom Menace (2009, background): “The argument in this paper is a generalization and extension of the results of Clarke (2005).”
- Democracy and Human Development (2012, background): “First, Ross includes a lagged dependent variable in most estimations, introducing the possibility of unit root problems and of confounding by way of colliders (Clarke 2005).”