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- 2025
- Article
Difference-in-Differences Subset Scan
By: Will Stamey, Sriram Somanchi and Edward McFowland III
Difference-in-differences (DiD) has been extensively applied in the literature to elicit the average causal effect of an intervention or policy. Though researchers explore heterogeneity in the treatment effect with respect to time or some observed covariate (usually... View Details
- 2025
- Working Paper
How Does Wage Inequality Affect the Labor Movement
By: Barbara Biasi, Zoë B. Cullen, Julia H. Gilman and Nina Roussille
This paper provides causal evidence on how wage inequality among workers affects the labor movement using three complementary research designs: a vignette experiment with union organizers, a natural policy experiment that increased wage inequality among Wisconsin... View Details
- July 2025
- Article
On the Economic Origins of Concerns Over Women’s Chastity
By: Anke Becker
This paper studies the origins and function of customs and norms that intend to keep women from being promiscuous. Using large-scale survey data from more than 100 countries, I test the anthropological theory that a particular form of preindustrial... View Details
- June 2025
- Article
Riding the Passion Wave or Fighting to Stay Afloat? A Theory of Differentiated Passion Contagion
By: Emma Frank, Kai Krautter, Wen Wu and Jon M. Jachimowicz
Prior research suggests that employees benefit from highly passionate teammates because passion spreads easily from one employee to the next. We develop theory to propose that life in high-passion teams may not be as uniformly advantageous as previously assumed. We... View Details
- May 2025
- Article
The Effects of Medical Debt Relief: Evidence from Two Randomized Experiments
By: Raymond Kluender, Neale Mahoney, Francis Wong and Wesley Yin
Two in five Americans have medical debt, nearly half of whom owe at least $2,500. Concerned by this burden, governments and private donors have undertaken large, high-profile efforts to relieve medical debt. We partnered with RIP Medical Debt (now Undue Medical Debt)... View Details
- 2025
- Working Paper
Discrimination, Rejection, and Job Search
By: Anne Boring, Katherine Coffman, Dylan Glover and María José González-Fuentes
We investigate how candidates’ willingness to apply responds to (potential) discrimination and rejection using a simulated labor market. Past work has shown that “blinding” job applications reduces discrimination and increases the rate at which women are hired. Our... 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
- 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
- 2024
- Working Paper
A Gender Backlash: Does Exposure to Female Labor Market Participation Fuel Gender Conservatism?
By: Paula Rettl, Diane Bolet, Catherine E. De Vries, Simone Cremaschi, Tarik Abou-Chadi and Sergi Pardos-Prado
The growing participation of women in the labor market has marked a significant societal transformation, coinciding with the rise of gender conservatism and far-right support. We study whether the economic consequences of labor market feminization and gender backlash... 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
- Working Paper
Patent Hunters
By: Lauren Cohen, Umit Gurun, Katie Moon and Paula Suh
Analyzing millions of patents granted by the USPTO between 1976 and 2020, we find a pattern where specific patents only rise to prominence after considerable time has passed. Amongst these late-blooming influential patents, we show that there are key players (patent... View Details
- August 2024
- Article
How Do Copayment Coupons Affect Branded Drug Prices and Quantities Purchased?
By: Leemore S. Dafny, Kate Ho and Edward Kong
Drug copayment coupons to reduce patient cost-sharing have become nearly ubiquitous for high-priced brand-name prescription drugs. Medicare bans such coupons on the grounds that they are kickbacks that induce utilization, but they are commonly used by... 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
- July–August 2024
- Article
Disclosing Downstream Emissions
By: Robert S. Kaplan and Karthik Ramanna
An increasing number of companies are using the E-liability carbon-accounting method as an important tool for tracking progress toward reducing global emissions in their supply chains. The system does not require formal accounting for downstream emissions—those... View Details
- 2024
- Working Paper
Navigating Software Vulnerabilities: Eighteen Years of Evidence from Medium and Large U.S. Organizations
By: Raviv Murciano-Goroff, Ran Zhuo and Shane Greenstein
How prevalent are severe software vulnerabilities, how fast do software users respond to the availability of secure versions, and what determines the variance in the installation distribution? Using the largest dataset ever assembled on user updates, tracking server... View Details
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
By: Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
- 2025
- Working Paper
Evaluations Amid Measurement Error: Determining the Optimal Timing for Workplace Interventions
By: Matthew DosSantos DiSorbo, Iavor I. Bojinov and Fiammetta Menchetti
Researchers have embraced factorial experiments to simultaneously evaluate multiple treatments, each with different levels. Typically, in large-scale factorial experiments, the primary objective is identifying the treatment with the largest causal effect, especially... View Details
- May 2024
- Article
Relational Attributions for One’s Own Resilience Predict Compassion for Others
By: Rachel Ruttan, Ting Zhang, Sivahn Barli and Katherine DeCelles
Existing work on attribution theory distinguishes between external and internal attributions (i.e., “I overcame adversity due to luck” vs. “my own effort”). We introduce the construct of relational resilience attributions (i.e., “due to help from other people”) as a... 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
- 2024
- Working Paper
Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference
By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov
Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details