Omitted-variable bias refers to the bias that occurs in statistical analyses, particularly in regression models, when a relevant variable is left out of the model. This can lead to incorrect estimates of the relationships between the included variables. When an important variable that affects both the dependent variable (the outcome) and one or more independent variables (the predictors) is omitted, it can cause the estimated coefficients of the included independent variables to be biased and inconsistent.
Articles by others on the same topic
There are currently no matching articles.