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- 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
- February 2025
- Article
Variation in Batch Ordering of Imaging Tests in the Emergency Department and the Impact on Care Delivery
By: Jacob C. Jameson, Soroush Saghafian, Robert S. Huckman and Nicole Hodgson
Objectives: To examine heterogeneity in physician batch ordering practices and measure the impact of a physician's tendency to batch order imaging tests on patient outcomes and resource utilization.
Study Setting and Design: In this retrospective study, we used... View Details
Study Setting and Design: In this retrospective study, we used... View Details
- January 2025
- Case
The VideaHealth AI Factory: CEO Florian Hillen on Speed, Scale, and Innovation (A)
By: Tsedal Neeley, Levi Stroud, Ruth Page and Dave Habeeb
Pre-abstract:
This multimedia case should be assigned to students in advance of class. Instructors should consider the timing of making the (B) Case videos available to students, as they may reveal key case details.
Abstract: Florian Hillen, co-founder... View Details
Abstract: Florian Hillen, co-founder... View Details
- January 2025
- Supplement
The VideaHealth AI Factory: CEO Florian Hillen on Speed, Scale, and Innovation (B)
By: Tsedal Neeley, Levi Stroud, Ruth Page and Dave Habeeb
Pre-abstract:
This multimedia case should be assigned to students in advance of class. Instructors should consider the timing of making the (B) Case videos available to students, as they may reveal key case details.
Abstract: Florian Hillen, co-founder... View Details
Abstract: Florian Hillen, co-founder... View Details
- 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
- December 2024
- Article
Proxy Advisory Firms and Corporate Shareholder Engagement
By: Aiyesha Dey, Austin Starkweather and Joshua White
We study how Institutional Shareholder Services (ISS) affect firms’ engagement with shareholders. Our analyses exploit a quasi-natural experiment using say-on-pay voting outcomes near a threshold that triggers ISS to review engagement activities. Firms receiving ISS... 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
- November 2024
- Article
The Health Costs of Cost Sharing
By: Amitabh Chandra, Evan Flack and Ziad Obermeyer
What happens when patients suddenly stop their medications? We study the health consequences of drug interruptions caused by large, abrupt, and arbitrary changes in price. Medicare’s prescription drug benefit as-if-randomly assigns 65-year-olds a drug budget as a... View Details
- October 2024
- Article
Medicare Part D Protected-Class Policy Is Associated with Lower Drug Rebates
By: Pragya Kakani, Michael Anne Kyle, Amitabh Chandra and Luca Maini
Medicare Part D does not allow plans to exclude drugs in six protected classes from their formularies, which may limit plans’ ability to negotiate rebates and lead to higher spending. We estimated the association between protected-class status, US-level estimated... View Details
- 2024
- Working Paper
Does Private Equity Have Any Business Being in the Health Care Business?
By: Nori Gerardo Lietz and Zirui Song
Private Equity (“PE”) has come under increased scrutiny by the press, academics, and policymakers, as well as the public, for its investments in health care delivery. This scrutiny has been exacerbated by recent high profile hospital bankruptcies following PE... View Details
- 2024
- Article
Learning Under Random Distributional Shifts
By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
Algorithmic assignment of refugees and asylum seekers to locations within host
countries has gained attention in recent years, with implementations in the U.S.
and Switzerland. These approaches use data on past arrivals to generate machine
learning models that can... View Details
- 2024
- Article
Crucibles, Multiple Sensitive Periods, and Career Progression
By: Prithwiraj Choudhury, Sunasir Dutta, Hise O. Gibson and Eric Lin
We study the effects of crucible experiences along multiple sensitive periods on career progression. While prior literature has hinted that individuals can be imprinted during multiple sensitive periods, not just during the early career, there has been scant attention... View Details
- 2024
- Article
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
By: Michael Lingzhi Li and Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
- August 2024
- Article
The Labor Market Effects of Loan Guarantee Programs
By: Jean-Noël Barrot, Thorsten Martin, Julien Sauvagnat and Boris Vallée
We investigate the labor market effects of a loan guarantee program targeting French SMEs during the financial crisis. Exploiting differences in regional treatment intensity in a border discontinuity design, we uncover a central trade-off for such interventions. While... 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
- 18 Apr 2024
- Lecture
Innovation Opportunities Created by COVID-19 Can Help: And How to Make Them Happen
The crush of patients created by COVID enabled the creation of sites for care outside the traditional hospital, such as retail pharmacies, ambulatory surgery centers, urgent care centers, telemedicine, and wireless sensors. Public policy mirrored these changes by... View Details
- 2024
- Working Paper
The Cram Method for Efficient Simultaneous Learning and Evaluation
By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details
Keywords: AI and Machine Learning
- 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
- 2024
- Working Paper
Design of Panel Experiments with Spatial and Temporal Interference
By: Tu Ni, Iavor Bojinov and Jinglong Zhao
One of the main practical challenges companies face when running experiments (or A/B tests) over a panel is interference, the setting where one experimental unit's treatment assignment at one time period impacts another's outcomes, possibly at the following time... View Details
Keywords: Research
- March 2024
- Article
Medicare Price Negotiation and Pharmaceutical Innovation Following the Inflation Reduction Act
By: Matthew Vogel, Pragya Kakani, Amitabh Chandra and Rena M. Conti
The Inflation Reduction Act (IRA) requires Medicare to negotiate lower prices for some medicines with high Medicare spending. Using historical data from public and proprietary sources to apply the IRA's negotiation criteria retrospectively, we identify all drugs that... View Details