A Convenient Omitted Variable Bias Formula for Treatment Effect Models

Damian Clarke

MPRA Paper from University Library of Munich, Germany

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, 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)
Date: 2018-03-10
New Economics Papers: this item is included in nep-ecm
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https://mpra.ub.uni-muenchen.de/85236/1/MPRA_paper_85236.pdf original version (application/pdf)

Related works:
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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