Combining Machine Learning with Econometric Analysis

Abstract:
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical
strategy uses predictive modeling techniques to “mine” variables of interest from available data, then includes those variables into an econometric framework to estimate causal effects. However, because the predictions from machine learning models are inevitably imperfect,
econometric analyses based on the predicted variables likely suffer from bias due to measurement error. I will present a stream of inter-connected work to understand the nature of this challenge and to design statistical approaches to correct for the estimation biases.

Bio:
Mochen Yang is an Associate Professor in the Department of Information and Decision Sciences at Carlson School of Management, University of Minnesota. He studies algorithmic decision-making from both a “make” perspective and a “use” perspective. From the make
perspective, he designs theoretically robust and computationally efficient algorithms to support decision-making in information-intensive environment. From the use perspective, he examines the antecedents of algorithmic decision-making as well as its impact on decision quality, fairness, and privacy. Before joining Carlson, he was an Assistant Professor in the Department of Operations and Decision Technologies at Kelley School of Business, Indiana University. He obtained my bachelor’s degree in Information Systems and Information Management from the School of Economics and Management at Tsinghua University.