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A convenient omitted variable bias formula for treatment effect models

Damian Clarke ()

Economics Letters, 2019, vol. 174, issue C, 84-88

Abstract: Generally, determining the size and magnitude of the omitted variable bias (OVB) in regression models is challenging when multiple included and omitted variables are present. Here, I describe a convenient OVB formula for treatment effect models with potentially many included and omitted variables. I show that in these circumstances it is simple to infer the direction, and potentially the magnitude, of the bias. In a simple setting, this OVB is based on mutually exclusive binary variables, however I provide an extension which loosens the need for mutual exclusivity of variables, deriving the bias in difference-in-differences style models with an arbitrary number of included and excluded “treatment” indicators.

Keywords: Omitted variable bias; Ordinary least squares regression; Treatment effects; Difference-in-differences (search for similar items in EconPapers)
JEL-codes: C21 C22 C13 (search for similar items in EconPapers)
Date: 2019
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Working Paper: A Convenient Omitted Variable Bias Formula for Treatment Effect Models (2018) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:174:y:2019:i:c:p:84-88

DOI: 10.1016/j.econlet.2018.10.035

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