Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions

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.