Abstract: When a researcher estimates the parameters of a regression function using information on all 50 states in the United States, or information on all visits to a website, what is the interpretation of the standard errors? Researchers typically report standard errors that are designed to capture sampling variation, based on viewing the data as a random sample drawn from a large population of interest, even in applications where it is difficult to articulate what that population of interest is and how it differs from the sample. In this paper we explore alternative interpretations for the uncertainty associated with regression estimates.
Guest Speaker Bio: Guido W. Imbens does research in econometrics and statistics. His research focuses on developing methods for drawing causal inferences in observational studies, using matching, instrumental variables, and regression discontinuity designs.
Guido Imbens is Professor of Economics at the Stanford Graduate School of Business. After graduating from Brown University, Guido taught at Harvard University, UCLA, and UC Berkeley. He joined the GSB in 2012. Imbens specializes in econometrics, and in particular methods for drawing causal inferences. Guido Imbens is a fellow of the Econometric Society and the American Academy of Arts and Sciences.