Causal inference and omitted variable bias in financial aid research: Assessing solutions. The Review of Higher Education

I gratefully acknowledge comments and advice from Latika Chaudhary, Dylan Conger, Amaury Nora, Ed St. John, two anonymous referees, and participants in the Lumina Conference on Barriers to Financial Aid and Access. I thank Phillip Stegner for excellent research assistance. This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college-going. I first describe the problem of self-selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed effects, difference-in-differences, regression discontinuity, and instrumental variables techniques in addressing the problem. I focus on the intuition, assumptions, and applications of each method in the context of the same research question, providing practical guidance for researchers interested in implementing these approaches. 1