Last week I had the opportunity to drop by RAND to interview Dr. Ben Miller. Ben is an economist who specializes in econometric modeling and is at the leading edge of applied data science within the think tank world. Ben offers many insights into causal inference, discovering patterns and uncovering signals in cloudy data. Ben explains the tools he uses, the methods he calls upon and the manner which he guides RAND’s “sponsors” to make informed decisions as they relate to national priorities.
As Ben says, “My toolkit is ‘applied econometrics,’ aka using data to estimate causal relationships. So think of techniques like instrumental variables, differences-in-differences, regression discontinuities, etc. Overall, it’s about putting a quantitative estimate on a relation between two variables in a way that is (hopefully!) unbiased, and at the same time understanding the uncertainty associated with that estimate. A really approachable and widely respected introduction to that toolkit is Angrist & Pischke’s ‘Mostly Harmless Econometrics.'”
Referenced works from the audio:
Bio: Ben Miller is an Associate Economist at the RAND Corporation and a Professor at the Pardee RAND Graduate School. His research spans a wide variety of topics including disaster mitigation, infrastructure finance, energy resilience, geospatial information, insurance, tax policy, regulatory affairs, health care supply, agriculture, econometrics, and beyond. Recent publications examine the link between flood insurance prices and housing affordability, review federal policies surrounding transportation and water infrastructure finance, estimate the causal impact of weather warning systems on fatalities and injuries from tornadoes, and overview econometric techniques for determining the value of geospatial information. Prior to joining RAND, Miller worked as a statistician supporting the U.S. Census Bureau’s Survey of Income and Program Participation. He holds a Ph.D. in economics from the University of California, San Diego and a B.S. in economics from Purdue University.