Problems in causal inference
Evan Starr is an Associate Professor (starting Fall 2021) of Management & Organization at the Robert H. Smith School of Business, University of Maryland.
The field of strategic management can benefit from a methods bootcamp that helps participants become better consumers and producers of empirical work. The primary content of the bootcamp deals with problems in causal inference, the methods that address them, and other issues in empirical research. The pedagogical approach relies heavily on simple econometric theory and Monte Carlo methods, emphasizing the key distinction between the data generating process and the estimated empirical model. The goal is not to provide a fully encompassing empirical methods course—which would be impossible in such a short period of time. Rather, the bootcamp is designed to introduce the central concepts, develop an intuitive grasp of key issues and ideas, and provide practical tools and the most up-to-date resources for participants.
- Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly Harmless Econometrics. Princeton University Press, 2008.
- Angrist, Joshua D., and Jörn-Steffen Pischke. Mastering ‘Metrics: The Path from Cause to Effect. Princeton University Press, 2014.
- Cunningham, Scott. Causal Inference, New Haven: Yale University Press, 2021.
- Starr, Evan, and Brent Goldfarb. “Binned scatterplots: A simple tool to make research easier and better.” Strategic Management Journal 41.12 (2020): 2261-2274.
- Wooldridge, Jeffrey M. Econometric Analysis of Cross Section and Panel Data. MIT Press, 2010.
- Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach. Cengage Learning, 2015.