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  • October–December 2022
  • Article

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
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  • August 2018 (Revised September 2018)
  • Supplement

LendingClub (B): Decision Trees & Random Forests

By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) case. In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan.
... View Details
Keywords: Data Science; Data Analytics; Decision Trees; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction
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  • Article

Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)

By: Eva Ascarza and Ayelet Israeli

An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details

Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
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  • 2020
  • Working Paper

Machine Learning for Pattern Discovery in Management Research

By: Prithwiraj Choudhury
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a... View Details
Keywords: Machine Learning; Theory Building; Induction; Decision Trees; Random Forests; K-nearest Neighbors; Neural Network; P-hacking; Analytics and Data Science; Analysis
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  • January 2021
  • Article

Machine Learning for Pattern Discovery in Management Research

By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect... View Details
Keywords: Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; ROC Curve; Confusion Matrix; Partial Dependence Plots; AI and Machine Learning
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  • November 2022
  • Article

A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups

By: Anjali M. Bhatt, Amir Goldberg and Sameer B. Srivastava
When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining... View Details
Keywords: Culture; Machine Learning; Natural Language Processing; Symbolic Boundaries; Organizations; Boundaries; Social Psychology; Interpersonal Communication; Organizational Culture
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  • 2021
  • Working Paper

An Empirical Study of Time Allotment and Delays in E-commerce Delivery

By: M. Balakrishnan, MoonSoo Choi and Natalie Epstein
Problem definition: We study how having more time allotted to deliver an order affects the speed of the delivery process. Furthermore, we seek to predict orders that are likely to be delayed early in the delivery process so that actions can be taken to avoid delays.... View Details
Keywords: Logistics; E-commerce; Mathematical Methods; AI and Machine Learning; Performance Productivity
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  • 04 Dec 2018
  • First Look

New Research and Ideas, December 4, 2018

follows Figel as she dives into the data to use it to build a model. Purchase this case:https://hbsp.harvard.edu/product/119020-PDF-ENG Harvard Business School Case 119-021 LendingClub (B): Decision Trees & Random View Details
Keywords: Dina Gerdeman
  • 09 Sep 2013
  • Lessons from the Classroom

Teaching Climate Change to Skeptics

A few years ago, Joseph B. Lassiter traveled to San Francisco, Houston, and New York to hold discussions with Harvard alumni on the topic of business and the environment. Each time, he surveyed the audience about the touchy subject of climate change and how society... View Details
Keywords: by Carmen Nobel
  • 08 Jan 2008
  • First Look

First Look: January 8, 2008

estimating the variance of demand using dispersion among experts' forecasts and scale. We test this methodology using three datasets, demand data at item level, sales data at firm level for retailers, and sales data at firm level for manufacturers. We show that the... View Details
Keywords: Martha Lagace

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