Simula@BI: An Introduction to Uses of (Causal) Machine Learning in Management and Economics Research

Advancements in machine learning (ML) and ease of access to high-computational power has fuelled interest in the potential applications of ML to management and economic research. This talk provides an overarching review of the literature on the uses of ML in management and economics research, and provides insights into its potential uses and future directions.

Associate Professor Ed Saiedi first differentiate the objectives of purely-predictive machine learning with econometric techniques aimed at causal inference. After drawing this distinction, he describes an overview and examples of recent uses of machine learning in management and economic literature, with a greater focus on economics, finance, strategy, entrepreneurship, and innovation research.

He then provides an overview of the early adoption and development of machine learning methods for causal inference in economics and management research. Here he will focus on methods that establish common ground between the previously disparate realms of causal inference and machine learning tools, exemplifying the use of causal ML with a conditionally-accepted Management Science study.

Finally, the presentation is to conclude with faculty discussions that draw on cross-disciplinary insights to discuss their overlap, co-evolution, and future directions.

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