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- July 2023
- Article
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted... View Details
- June 2023
- Case
Accounting for Loan Losses at JPMorgan Chase: Predicting Credit Costs
By: Jonas Heese, Jung Koo Kang and James Weber
The case examines the accounting for loan losses at a large bank, how a bank sets its Allowance for Loan and Lease Losses (ALLL) on its financial statements. ALLL, and the rules that set them, determine when banks would and would not extend loans, which significantly... View Details
- 2023
- Working Paper
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation
By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor Bojinov
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. In addition to augmenting managers’ decision-making, experimentation mitigates risk by limiting the proportion of customers exposed to... View Details
- 2023
- Working Paper
Auditing Predictive Models for Intersectional Biases
By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we... View Details
- June 2023 (Revised February 2024)
- Case
Betting on Green Steel
By: George Serafeim and Sofoklis Melissovas
'Betting on Green Steel' traces the innovative journey embarked upon by a group of MBA students who have set out to conceive a novel steelmaker that pioneers the production of green steel. The ensemble is confronted with a series of critical choices that will shape the... View Details
- 2023
- Article
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in... View Details
- May 2023 (Revised June 2023)
- Case
Harvard University and Urban Mining Industries: Decarbonizing the Supply Chain
By: Shirley Lu and Robert S. Kaplan
The case describes Harvard University’s consideration to decarbonize its supply chain by replacing cement with a low-carbon substitute called Pozzotive®. Developed and produced by Urban Mining Industries, Pozzotive® is a ground-glass material made with post-consumer... View Details
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
- 2024
- Working Paper
Using LLMs for Market Research
By: James Brand, Ayelet Israeli and Donald Ngwe
Large language models (LLMs) have rapidly gained popularity as labor-augmenting
tools for programming, writing, and many other processes that benefit from quick text
generation. In this paper we explore the uses and benefits of LLMs for researchers and
practitioners... View Details
- 2023
- Article
Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations.
By: Edward McFowland III and Cosma Rohilla Shalizi
Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node’s network partners being informative about the node’s attributes and therefore its... View Details
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
- March–April 2023
- Article
Pricing for Heterogeneous Products: Analytics for Ticket Reselling
By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details
- 2023
- Working Paper
PRIMO: Private Regression in Multiple Outcomes
By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates... View Details
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details
- 2023
- Working Paper
Remote Work across Jobs, Companies, and Space
By: Stephen Hansen, Peter John Lambert, Nick Bloom, Steven J. Davis, Raffaella Sadun and Bledi Taska
The pandemic catalyzed an enduring shift to remote work. To measure and characterize
this shift, we examine more than 250 million job vacancy postings across five
English-speaking countries. Our measurements rely on a state-of-the-art language-processing
framework... View Details
- 2023
- Article
Bridging the Gap with the ‘New’ Economic History of Africa
By: Ewout Frankema and Marlous van Waijenburg
This review article seeks to build bridges between mainstream African history and the more historically oriented branch of the ‘new’ economic history of Africa. We survey four central topics of the new economic history of Africa—growth, trade, labor, and inequality—and... View Details
Keywords: Economic Growth; Trade; Labor; Equality and Inequality; Development Economics; History; Africa
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market... View Details
- March 2023
- Article
Not from Concentrate: Collusion in Collaborative Industries
By: Jordan M. Barry, John William Hatfield, Scott Duke Kominers and Richard Lowery
The chief principle of antitrust law and theory is that reducing market concentration—having more, smaller firms instead of fewer, bigger ones—reduces anticompetitive behavior. We demonstrate that this principle is fundamentally incomplete.
In many... View Details
In many... View Details
Keywords: Antitrust; Antitrust Law; Antitrust Theory; Law And Economics; Collusion; Collaboration; Collaborative Industries; Regulation; ＂Repeated Games＂; IPOs; Initial Public Offerings; Underwriters; Real Estate; Real Estate Agents; Realtors; Syndicated Markets; Syndication; Brokers; Market Concentration; Competition; Law; Economics; Collaborative Innovation and Invention; Governing Rules, Regulations, and Reforms; Game Theory; Initial Public Offering
- 2023
- Working Paper
Accounting for Carbon Offsets – Establishing the Foundation for Carbon-Trading Markets
By: Robert S. Kaplan, Karthik Ramanna and Marc Roston
Tackling climate change requires reductions in current and future greenhouse gas (GHG) emissions as well as the removal of existing GHG from the atmosphere. Carbon-offset producers purport to provide such removals. But poor measurement practices and inadequate controls... View Details
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details