Working Paper | HBS Working Paper Series | 2019

Machine Learning Complementarities with Regression Analysis: Selection, Evaluation, and Interpretation of Machine Learning Methods for Exploratory Data Analysis and Pattern Detection

by Prithwiraj Choudhury, Ryan Allen and Michael G. Endres

Abstract

Machine learning (ML) methods could have a larger footprint in strategy and management research if used to conduct exploratory data analysis and to identify robust patterns in the data that are relatively difficult to identify using traditional methods. Compared to traditional causal inference methods, ML algorithms make far fewer a priori assumptions about the functional form of the underlying model that best represents the data, thus researchers could use ML methods to explore novel and robust patterns in data. Once identified, these patterns can be tested using traditional econometric methods. Using a motivating example, we demonstrate how to implement three specific ML algorithms (decision trees, random forests, and neural networks) to reveal patterns in data and offer guidance on selecting ML algorithms. We also demonstrate how to use visual tools to evaluate the resulting model performance and interpret predictions of the model to illuminate patterns in the data. We also provide sample code to employ ML algorithms used in the paper.

Keywords: Machine learning; theory building; induction; Decision Trees; random forests; k-nearest neighbors; neural network; p-hacking; Data and Data Sets; Analysis;

Language: English Format: Print 45 pages Read Now

Citation:

Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning Complementarities with Regression Analysis: Selection, Evaluation, and Interpretation of Machine Learning Methods for Exploratory Data Analysis and Pattern Detection." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised May 2019.)

About the Authors

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Prithwiraj Choudhury
Lumry Family Associate Professor of Business Administration
Technology and Operations Management

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