Modeling Complex Systems for Policy Analysis

Econometrics, biostatistics, psychometrics, sociometrics, machine learning—all start from the same principles and there are plenty of shared tools. Econometrics emphasizes causal inference with observational data, which is a natural fit for policy analysis where experimental opportunities are limited and yet the goal is to predict changes in a system. Biostatistics (but not epidemiology) deals with experimental design and analysis, psychometrics with measurement and deriving conceptual ideas, machine learning with predictions. All fields change over time and are prone to fads, but there is deep and stable foundation that is shared by all of them. As you are in an interdisciplinary program, it is important that you see the relationship between similar approaches across disciplines.

Students should have taken the core econometric/statistics sequence before trying this course. A good understanding of linear regression and basic probability theory is needed or you will not get any traction. The mathematical level is not that high, lower than in a graduate econometrics or statistics course, and we do not discuss theorems or prove them. But that doesn’t mean the concepts are easy, the concepts are still hard.