A Convenient Omitted Variable Bias Formula for Treatment Effect Models
Damian Clarke ()
MPRA Paper from University Library of Munich, Germany
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, and derives 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: C13 C21 C22 (search for similar items in EconPapers)
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Journal Article: A convenient omitted variable bias formula for treatment effect models (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:85236
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