Machine Learning Economist
Areas of Expertise
The availability of big data has provided new avenues for how social scientists find meaning in it. Simon noticed that many techniques in the traditional econometric toolkit are designed for moderately sized data sets and are ill equipped to deal with the increased size, complexity, and dimensionality of today's research data sets. Simon explores the new opportunities and challenges that big-data analysis creates.
Simon also works on causal inference in panel data models. For example, in his 2019 paper “Pre-event Trends in the Panel Event-study Design,” published in the American Economic Review, he developed a novel way to estimate the causal effect of an event that is valid even when endogeneity of the event leads to pre-event trends in the outcome.
Lately, Simon has been very interested in discrimination and algorithmic fairness and is part of an ongoing collaboration with researchers at the University of Pennsylvania on this subject.
Simon joined the Reserve Bank in 2018 after finishing his Ph.D. in economics at Brown University. He has an M.Sc. in economics and a B.S. in econometrics and operations research from Maastricht University.