Multivariate Regression Controls

These flashcards cover key terms and concepts from a lecture on Multivariate Regression Controls.

Omitted Variable Bias (OVB)

A type of bias that occurs when a model leaves out one or more relevant variables, which can lead to erroneous conclusions.

Good Controls

Variables that help eliminate sources of spurious correlation and reduce standard errors in regression analysis.

Bad Controls

Variables that can increase selection bias and produce misleading results in regression analysis.

Confounder

A variable that affects both treatment and outcome, creating a spurious association if not controlled.

Collider

A variable that is caused by both treatment and outcome; controlling for it induces bias.

Categorical Variables

Variables that represent characteristics divided into discrete categories; often converted to binary indicators for regression.

Perfect Collinearity

A situation in regression analysis where two or more predictor variables are perfectly correlated, making it impossible to estimate their individual effects.

Causal Effect

The impact of a treatment or variable on an outcome.

Spurious Correlation

A relationship between two variables that appear to be related but are actually both influenced by a third variable.

Direction of Omitted Variable Bias

The way in which omitting a variable from a model can distort the estimation of the effect of another variable.