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Publications
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- 2025
- Working Paper
Extractive Taxation and the French Revolution
By: Tommaso Giommoni, Gabriel Loumeau and Marco Tabellini
We study the fiscal determinants of the French Revolution, exploiting plausibly exogenous variation in the salt tax—a large source of royal revenues and one of the most extractive forms of taxation of the Ancien Régime. Implementing a Regression Discontinuity... View Details
- 2025
- Working Paper
Tax Planning, Illiquidity, and Credit Risks: Evidence from DeFi Lending
By: Lisa De Simone, Peiyi Jin and Daniel Rabetti
This study establishes a plausible causal link between tax-planning-induced illiquidity and credit risks in lending markets. Exploiting an exogenous tax shock imposed by the Internal Revenue Service (IRS) on cryptocurrency gains, along with millions of transactions in... View Details
- 2025
- Working Paper
Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning
Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships
between customer states, company actions, and long-term value. However, its practical implementation often faces significant
challenges.... View Details
- November 2024
- Article
Preference Externality Estimators: A Comparison of Border Approaches and IVs
By: Xi Ling, Wesley R. Hartmann and Tomomichi Amano
This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from “preference externalities,” following the intuition in Waldfogel (2003). We highlight two dimensions in... View Details
- 2024
- Working Paper
When Batteries Meet Hydrogen: Dual-Storage Investments for Load-Shifting Purposes
By: Christian Kaps and Simone Marinesi
Power systems account for nearly 40% of global emissions. As the world tries to reduce emissions by increasing renewable penetration, storage technologies are playing an increasingly important role in matching variable renewable supply with demand. Batteries have... View Details
- September 2024
- Case
Eat App: Building and Monetizing an End-to-End Dining Experience Solution
By: Elie Ofek and Ahmed Dahawy
Founded in 2015 in Bahrain, Eat App was an up-and-coming player in the global restaurant management software business. In early 2024, having shifted to a product-led growth strategy, the company’s co-founders faced a host of decisions that could greatly impact their... View Details
- July–August 2024
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to... View Details
- April 2024
- Article
Demand-and-Supply Imbalance Risk and Long-Term Swap Spreads
By: Samuel G. Hanson, Aytek Malkhozov and Gyuri Venter
We develop and test a model in which swap spreads are determined by end users' demand for
and constrained intermediaries’ supply of long-term interest rate swaps. Swap spreads reflect
compensation both for using scarce intermediary capital and for bearing convergence... View Details
- January 2024
- Supplement
Winning Business at Russell Reynolds
By: Ethan Bernstein and Cara Mazzucco
In an effort to make compensation drive collaboration, Russell Reynolds Associates’ (RRA) CEO Clarke Murphy sought to re-engineer the bonus system for his executive search consultants in 2016. As his HR analytics guru, Kelly Smith, points out, that risks upsetting—and... View Details
- 2025
- Working Paper
Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach
By: Ta-Wei Huang and Eva Ascarza
As firms increasingly rely on customer data for personalization, concerns over privacy and regulatory compliance have grown. Local Differential Privacy (LDP) offers strong individual-level protection by injecting noise into data before collection. While... View Details
- December 2023
- Article
When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments
By: Christian Kaps, Simone Marinesi and Serguei Netessine
Globally, 1.5 billion people live off the grid, their only access to electricity often limited to operationally-expensive fossil fuel generators. Solar power has risen as a sustainable and less costly option, but its generation is variable during the day and... View Details
- 2023
- Working Paper
Causal Interpretation of Structural IV Estimands
By: Isaiah Andrews, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan and Jesse M. Shapiro
We study the causal interpretation of instrumental variables (IV) estimands of nonlinear, multivariate structural models with respect to rich forms of model misspecification. We focus on guaranteeing that the researcher's estimator is sharp zero consistent, meaning... View Details
Keywords: Mathematical Methods
- 2025
- Working Paper
Residential Battery Storage - Reshaping the Way We Do Electricity
By: Christian Kaps and Serguei Netessine
In this paper, we aim to understand when private households invest in behind-the-meter battery storage next to rooftop solar and how those batteries impact households, the electricity market, and emissions. We answer three main research questions: 1) When do customers... View Details
- June 2023 (Revised September 2023)
- Simulation
Managing the Customer Journey Marketing Simulation: Adobe's Data-Driven Operating Model (DDOM)
By: Sunil Gupta, Rajiv Lal and Celine Chammas
Adobe started monitoring Annual Recurring Revenue (ARR), one of its primary metrics, when it shifted from selling its software in a box to selling the software as a subscription-based cloud service. They wanted to know when, where, and how much to invest in marketing.... View Details
- April 2023 (Revised September 2023)
- Case
Levels: The Remote, Asynchronous, Deep Work Management System
By: Joseph B. Fuller and George Gonzalez
Levels is a highly innovative startup in the health care space. They intend to revolutionize health by linking behavior—eating, exercise, sleeping, etc.—to changes in metabolism. They believe metabolic health can be managed through careful monitoring of changes in... View Details
- April 2023
- Article
Inattentive Inference
By: Thomas Graeber
This paper studies how people infer a state of the world from information structures that include additional, payoff-irrelevant states. For example, learning from a customer review about a product’s quality requires accounting for the reviewer’s otherwise irrelevant... 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
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
- December 2022
- Case
Mission Produce in 2022
By: Forest Reinhardt, Jose B. Alvarez and Natalie Kindred
Founded by CEO Steve Barnard in 1983, California-based Mission Produce was a leading supplier of Hass avocados with a global sourcing, marketing, and distribution network and $892 million in 2021 sales. Barnard had been influential in the global avocado trade’s... View Details
- 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