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- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... 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
- 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
- 2023
- Working Paper
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
By: Biyonka Liang and Iavor I. Bojinov
Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
- 2025
- Working Paper
Exports in Disguise? Trade Rerouting During the U.S.-China Trade War
By: Ebehi Iyoha, Edmund Malesky, Jaya Wen and Sung-Ju Wu
This paper introduces a new measure of tariff evasion through rerouting and applies it to the
2018 U.S.–China trade war, focusing on Vietnam as a transit country. We use transaction-level trade data and define rerouting as the flow of a granular eight-digit Harmonized... 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
- 2024
- Working Paper
The Uneven Impact of Generative AI on Entrepreneurial Performance
By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
Scalable and low-cost AI assistance has the potential to improve firm decision-making and economic performance. However, running a business involves a myriad of open-ended problems, making it difficult to know whether recent AI advances can help business owners make... View Details
- 2023
- Working Paper
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
By: Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine... 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
- 2023
- Article
Experimental Evaluation of Individualized Treatment Rules
By: Kosuke Imai and Michael Lingzhi Li
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details
- 2022
- Working Paper
A Linear Panel Model with Heterogeneous Coefficients and Variation in Exposure
By: Jesse M. Shapiro and Liyang Sun
Linear panel models featuring unit and time fixed effects appear in many areas of empirical economics. An active literature studies the interpretation of the ordinary least squares estimator of the model, commonly called the two-way fixed effects (TWFE) estimator, in... View Details
- March 2022
- Article
Where to Locate COVID-19 Mass Vaccination Facilities?
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li and Alessandro Previero
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in particular, the new... View Details
- February 2022
- Case
Leading The UK Vaccine Task Force
By: Amy C. Edmondson and Claudia Pienica
This case describes the first six months of the UK Vaccine Taskforce, under the leadership of Kate Bingham. With a career spent in the private sector as a biotech investor, Bingham’s appointment within the government was considered unusual. The overarching brief given... View Details
Keywords: COVID-19; Vaccine; Government; Health Pandemics; Health Care and Treatment; Science; Innovation and Invention; Groups and Teams; Leadership; Decision Making; Government and Politics; Health; Innovation and Management; Governance; Change; Government Administration; Health Industry; Financial Services Industry; Public Administration Industry; Europe; United Kingdom
- Article
A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects
By: Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun
We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility maximizing context, public... View Details
- November 2021
- Article
Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective
By: Iavor Bojinov, Ashesh Rambachan and Neil Shephard
In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative... View Details
- November 2021
- Article
Ratings, Reviews, and the Marketing of New Products
By: Itay P. Fainmesser, Dominique Olié Lauga and Elie Ofek
We study how user-generated content (UGC) about new products impacts a firm's advertising and pricing decisions and the effect on profits and market dynamics. We construct a two-period model where consumers value quality and are heterogeneous in their taste for the new... View Details
- Article
A Feasibility Study Using Time-driven Activity-based Costing as a Management Tool for Provider Cost Estimation: Lessons from the National TB Control Program in Zimbabwe in 2018
By: J. Chirenda, B. Nhlema Simwaka, C. Sandy, K. Bodnar, S. Corbin, P. Desai, T. Mapako, S. Shamu, C. Timire, E. Antonio, A. Makone, A. Birikorang, T. Mapuranga, M. Ngwenya, T. Masunda, M. Dube, E. Wandwalo, L. Morrison and R. S. Kaplan
Background: This study used process maps and time-driven activity-based costing to document TB service delivery processes. The analysis identified the resources required to sustain TB services in Zimbabwe, as well as several opportunities for more effective and... View Details
- Article
Nudging: Progress to Date and Future Directions
By: John Beshears and Harry Kosowsky
Nudges influence behavior by changing the environment in which decisions are made, without restricting the menu of options and without altering financial incentives. This paper assesses past empirical research on nudging and provides recommendations for future work in... View Details
- 2020
- Working Paper
Targeting for Long-Term Outcomes
By: Jeremy Yang, Dean Eckles, Paramveer Dhillon and Sinan Aral
Decision makers often want to target interventions so as to maximize an outcome that is observed only in the long term. This typically requires delaying decisions until the outcome is observed or relying on simple short-term proxies for the long-term outcome. Here we... View Details
- August 2020
- Article
Workplace Knowledge Flows
By: Jason Sandvik, Richard Saouma, Nathan Seegert and Christopher Stanton
We conducted a field experiment in a sales firm to test whether improving knowledge flows between coworkers affects productivity. Our design allows us to compare different management practices and to isolate whether frictions to knowledge transmission primarily reside... View Details