Business Commons^™

Full Disclosure: Model Uncertainty In Adjusting For Confounders, Dieudonne Dusenge

Graduate Theses and Dissertations

This study examines the role of knowledge about underlying causal relationships in classifying controls in order to mitigate omitted- and included-variable biases. Using simulations and accounting examples, the study shows that the researcher may not distinguish good and bad controls because the underlying causal relationships are unobservable, and remedying strategies (such as the relative timing of measurement) may not remove the uncertainty in the classification. Because of the uncertainty about which controls to use, two or more models will be credible as will the distinct estimates derived from them. Next, the study shows that the current standard practice of singling …