Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/273051 
Year of Publication: 
2022
Series/Report no.: 
EERI Research Paper Series No. 05/2022
Publisher: 
Economics and Econometrics Research Institute (EERI), Brussels
Abstract: 
In observational studies, confounding variables that affect both the exposure and an outcome of interest are a general concern. It is well known that failure to control for confounding variables adequately can worsen inference on an exposure's effect on outcome. In this paper, we explore how exposure effect inference changes when non-confounding covariates are added to the assumed logistic regression model, after the set of all true confounders are included. This is done via an exhaustive simulation study with thousands of randomly generated scenarios to make general statements about over-adjusting in logistic regression. Our results show that in general, adding non-confounders to the regression model decreases the mean squared error for non-null exposure effects. The probability of both type I and type II errors also decrease with addition of more covariates given that all true confounders are controlled for.
Subjects: 
regression model
confounding covariates
type I errors
type II errors
JEL: 
C12
C13
C15
Document Type: 
Working Paper

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