About

Professor Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She holds a bachelor’s degree from Duke University and a PhD from Stanford University, and she also holds an honorary doctorate from Duke University. Her research focuses on the economics of digitization, marketplace design, and the intersection of machine learning and econometrics. She has studied a range of application areas including timber auctions, online advertising, the news media, labor market transitions, health, and digital technology for social impact. Professor Athey has served as a consulting chief economist for Microsoft Corporation for six years and has been involved with multiple private and public technology firms' boards. She was a founding associate director of the Stanford Institute for Human-Centered Artificial Intelligence and is the founding director of the Golub Capital Social Impact Lab at Stanford GSB. Her career includes academic appointments at Stanford, Harvard, and MIT, and she has received numerous awards and honors such as the John Bates Clark Medal, the R.K. Cho Economics Prize, and election to the National Academy of Science and the American Academy of Arts and Sciences. She served as the 2023 President of the American Economics Association and has been actively involved in professional service roles, including editorial positions and committee memberships.

Research topics

• Computer Science
• Medicine
• Machine Learning
• Artificial Intelligence
• Internal medicine
• Data science
• Political Science
• Management science
• Microeconomics
• Virology
• Biology
• Economics
• Market economy
• Engineering
• Immunology
• Econometrics
• Engineering ethics
• Business
• Geography
• Industrial organization
• Public economics
• Computational biology
• Environmental health
• Internet privacy

Selected publications

• The Economics of Algorithmic Personalization: Evidence from an Educational Technology Platform

  SSRN Electronic Journal · 2026-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Estimating Variances for Causal Panel Data Estimators

  ArXiv.org · 2025-10-13

  preprintOpen access

  There has been a recent surge in research on causal panel data models, leading to many new estimators for average causal effects. However, researchers have paid less attention to quantifying the precision of these estimators. This paper addresses that gap by studying the problem of variance estimation in causal panel settings. We develop a unified framework for comparing the three main variance estimators used in these settings: regression-based, Unit-Placebo, and Time-Placebo estimators. We show that each relies on a distinct exchangeability assumption and, correspondingly, each targets a different conditional variance. We find that, under some assumptions, all three estimators are all valid, but that their statistical power differs substantially depending on the heteroskedasticity present in the data. Building on these insights, we propose a new variance estimator that flexibly accounts for heteroskedasticity across the unit and time dimensions, and delivers superior statistical power in realistic panel data settings.

  PublisherOA PDFDOI

• Does Q&A Boost Engagement? Health Messaging Experiments in the United States and Ghana

  Management Science · 2025-08-28

  articleOpen access

  Effective information sharing is critical for the success of organizations and governments. Because information that is easy to access is more likely to be adopted, leaders often minimize friction in information delivery. However, one type of friction may increase engagement: piquing curiosity by posing relevant questions prior to sharing information. To test this, we shared identical information about COVID-19 in either question-and-answer format or via direct statements across two preregistered field experiments in Ghana and Michigan (total n = 49,395). Q&A-style communication increased information seeking about directly related topics (e.g., how to wear a mask properly) by 1.0 percentage point (216%) in Ghana and by 1.1 percentage points (19%) in Michigan (p’s < 0.001) and increased self-reported behavior change by 1.3 percentage points (4%) in Michigan (p = 0.002). However, sharing information in Q&A format did not increase interest in general COVID-19 information in either setting, suggesting that the impact of Q&A-style messaging on information seeking may be issue specific. In Michigan, both Q&A-style and direct statement messaging produced less information seeking than sending no informational messages, likely because of differential attrition: the more texts participants received, the more likely they were to opt out of receiving messages, which made it impossible for them to seek more information via text. In a follow-up implementation experiment with social media ads (a messaging strategy without attrition challenges), Q&A-style ads generated 9%–11% more unique clicks to the CDC website per dollar spent than ads that directly stated information about vaccines (p < 0.001). We speculate that Q&A-style information delivery may stimulate curiosity, driving its benefits. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: The authors thank the National Science Foundation [RAPID Grant 2033321], the Bill and Melinda Gates Foundation, Northwestern University’s Global Poverty Research Lab, Stanford University’s Golub Capital Social Impact Lab, Harvard Business School, the University of Pennsylvania, the AKO Foundation, John Alexander, Mark J. Leder, and Warren G. Lichtenstein for funding support. This work was also supported by Grand Challenges in Global Health. Supplemental Material: The supplementary materials and data files are available at https://doi.org/10.1287/mnsc.2024.04405 .

  PublisherOA PDFDOI

• Artificial Intelligence, Competition, and Welfare

  National Bureau of Economic Research · 2025-11-01 · 1 citations

  reportOpen access1st authorCorresponding

  We study how market power in artificial intelligence (AI) shapes wages and welfare in openeconomy general equilibrium by treating AI as a priced, imported factor.Across three models, we separate technical efficiency from the impact of upstream price setting.In a two-traded-goods benchmark, the incidence of AI price changes depends on how sectoral skill intensity changes with AI prices; non-monotone intensity can generate "double harm" for unskilled workers (lower real wage after a large decrease in the price of AI, and real wage decreases further when the AI price rises as a result of market power).With one non-traded sector, we observe that the classic "Dutch disease" effect here would arise when one sector gets more productive and draws labor away from other sectors, creating scarcity and raising prices; but this is not what we expect from the introduction of labor-substituting AI.In contrast, our last model considers two non-traded sectors and CES/free entry, and the opportunity for discrete adoption of technology that replaces unskilled labor from the AI-using sector.When AI reduces unit costs and increases variety, it will not pull U from non-tradables, instead it will displace workers from the AI-using sector and lower wage due to diminishing returns in alternative sectors.Strategic upstream pricing of AI then harms welfare through unit-cost (usage fees) and variety (access fees) channels, with income leakage abroad.We derive an adoption frontier tying feasible usage prices to displaced workers' outside options and show a monopolist typically prices on this boundary; capping one instrument shifts rents to the other.Broad gains for the adopting country relies on pressure (or regulation) on both usage and access fees and as well as policy that supports productive absorption of displaced labor.The framework clarifies when AI can lower real wages and aggregate welfare despite efficiency gains.

  PublisherDOI

• Does Q&A Boost Engagement? Health Messaging Experiments in the U.S. and Ghana

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• The Experimental Selection Correction Estimator: Using Experiments to Remove Biases in Observational Estimates

  SSRN Electronic Journal · 2025-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal

  Journal of Econometrics · 2025-01-13 · 8 citations

  article1st author
  PublisherDOI

• Systemic Cyber Risk: Linking Measurement, Market Incentives, and Policy

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Does Q&A Boost Engagement? Health Messaging Experiments in the U.S. and Ghana

  National Bureau of Economic Research · 2025-01-01 · 1 citations

  reportOpen access
  PublisherDOI

• Does Q&A Boost Engagement? Health Messaging Experiments in the U.S. and Ghana

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

Recent grants

• Comparative Statics: Theory and an Empirical Framework for Testing Predictions

  NSF · $160k · 1996–1999

• Private Information in Dynamic Games

  NSF · $226k · 2004–2010

Frequent coauthors

• Guido W. Imbens

  Stanford University

  222 shared

• Patrick J. Kehoe

  81 shared

• Andrew Atkeson

  University of California, Los Angeles

  75 shared

• Stefan Wager

  64 shared

• Alberto Abadie

  49 shared

• Jeffrey M. Wooldridge

  Michigan State University

  48 shared

• Jonathan Levin

  RAND Corporation

  44 shared

• Michael Luca

  William Carey University

  34 shared

Education

• PhD, Graduate School of Business

  Stanford University

  1995

Awards & honors

• R. K. Cho Economics Prize (2024)
• Honorary degree, London Business School (2022)
• Adam Smith Award, National Association of Business Economist…
• CME Group-Mathematical Sciences Research Institute Prize in…
• R. Michael and Mary Shanahan Faculty Fellow for 2020–21