The Causal Structure of Suppressor Variables

Abstract

Suppression effects in multiple linear regression are one of the most elusive phenomena in the educational and psychological measurement literature. The question is, How can including a variable, which is completely unrelated to the criterion variable, in regression models significantly increase the predictive power of the regression models? In this article, we view suppression from a causal perspective and uncover the causal structure of suppressor variables. Using causal discovery algorithms, we show that classical suppressors defined by Horst (1941) are generated from causal structures which reveal the equivalence between suppressors and instrumental variables. Although the educational and psychological measurement literature has long recommended that researchers include suppressors in regression models, the causal inference literature has recently recommended that researchers exclude instrumental variables. The conflicting views between the two disciplines can be resolved by considering the different purposes of statistical models, prediction and causal explanation.