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

    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,... View Details
    • 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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    • Awards

    Runner-Up for Best Paper Award, INFORMS Workshop on Data Science, 2018

    By: Edward McFowland III
    Runner Up for the 2018 Best Paper Award at the INFORMS Workshop on Data Science for "Using Data-Mined Variables in Causal Inference Tasks: A Random Forest Approach to the Measurement Error Problem" with Mochen Yang, Gordon Burtch, and Gediminas Adomavicius. View Details

      Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT) - PNAS

      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... View Details

      • 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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      • Web

      Technology & Operations Management Awards & Honors - Faculty & Research

      . Edward McFowland III : Runner Up for the 2018 Best Paper Award at the INFORMS Workshop on Data Science for "Using Data-Mined Variables in Causal Inference Tasks: A Random Forest Approach to the Measurement... View Details
      • 01 Mar 2024
      • News

      Alumni and Faculty Books and Podcasts

      rather than vilified. What Have They Done By Benjamin Campbell (MBA 1992) Teutoburg Forest Press Set in 2023, What Have They Done is the sequel to the critically acclaimed Nothing They Won’t Do. It’s five years later, and Putin and Iran... View Details
      Keywords: Publishing Industries (except Internet); Information
      • 12 Jan 2023
      • News

      ‘Debiasing’ Debt with Data

      records on borrowers, and put the data through a random forest regression, a type of analysis known for producing good predictions that can be readily understood. When they tested the resulting model, the... View Details
      Keywords: Ralph Ranalli
      • 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
      • 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