Estimating the causal effect of measured endogenous variables: A tutorial on experimentally randomized instrumental variables

Omitted variables create endogeneity and thus bias the estimation of the causal effect of measured variables on outcomes. Such measured variables are ubiquitous and include perceptions, attitudes, emotions, behaviors, and choices. Even experimental studies are not immune to the endogeneity problem. I propose a solution to this challenge: Experimentally randomized instrumental variables (ERIVs), which can correct for endogeneity bias via...