2020-21 Courses

Stanford Advisees

All Publications

  • Preventing cytokine storm syndrome in COVID-19 using α-1 adrenergic receptor antagonists. The Journal of clinical investigation Konig, M. F., Powell, M. A., Staedtke, V., Bai, R. Y., Thomas, D. L., Fischer, N. M., Huq, S., Khalafallah, A. M., Koenecke, A., Xiong, R., Mensh, B., Papadopoulos, N., Kinzler, K. W., Vogelstein, B., Vogelstein, J. T., Athey, S., Zhou, S., Bettegowda, C. 2020


    Medications that target catecholamine-associated inflammation may prevent cytokine storm syndrome associated with COVID-19 and other diseases.

    View details for DOI 10.1172/JCI139642

    View details for PubMedID 32352407

  • Economists (and Economics) in Tech Companies JOURNAL OF ECONOMIC PERSPECTIVES Athey, S., Luca, M. 2019; 33 (1): 209–30
  • Comment on: "The Blessings of Multiple Causes" by Yixin Wang and David M. Blei JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Athey, S., Imbens, G. W., Pollmann, M. 2019; 114 (528): 1602–4
  • GENERALIZED RANDOM FORESTS ANNALS OF STATISTICS Athey, S., Tibshirani, J., Wager, S. 2019; 47 (2): 1148–78

    View details for DOI 10.1214/18-AOS1709

    View details for Web of Science ID 000455476800018

  • Machine Learning Methods That Economists Should Know About ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019 Athey, S., Imbens, G. W., Aghion, P., Rey, H. 2019; 11: 685–725
  • Balanced Linear Contextual Bandits Dimakopoulou, M., Zhou, Z., Athey, S., Imbens, G., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2019: 3445–53
  • Approximate residual balancing: debiased inference of average treatment effects in high dimensions JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY Athey, S., Imbens, G. W., Wager, S. 2018; 80 (4): 597–623

    View details for DOI 10.1111/rssb.12268

    View details for Web of Science ID 000442217900001

  • Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data Athey, S., Blei, D., Donnelly, R., Ruiz, F., Schmidt, T. AMER ECONOMIC ASSOC. 2018: 64–67
  • The Impact of Consumer Multi-homing on Advertising Markets and Media Competition MANAGEMENT SCIENCE Athey, S., Calvano, E., Gans, J. S. 2018; 64 (4): 1574–90
  • The value of information in monotone decision problems RESEARCH IN ECONOMICS Athey, S., Levin, J. 2018; 72 (1): 101–16
  • Estimation and Inference of Heterogeneous Treatment Effects using Random Forests JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Wager, S., Athey, S. 2018; 113 (523): 1228–42
  • Stable Prediction across Unknown Environments Kuang, K., Cui, P., Athey, S., Xiong, R., Li, B., ACM ASSOC COMPUTING MACHINERY. 2018: 1617–26
  • Learning in Games with Lossy Feedback Zhou, Z., Mertikopoulos, P., Athey, S., Bambos, N., Glynn, P., Ye, Y., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • Exact p-Values for Network Interference JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Athey, S., Eckles, D., Imbens, G. W. 2018; 113 (521): 230–40
  • Beyond prediction: Using big data for policy problems. Science (New York, N.Y.) Athey, S. 2017; 355 (6324): 483–85


    Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.

    View details for PubMedID 28154050

  • Context Selection for Embedding Models Liu, L., Ruiz, F. R., Athey, S., Blei, D. M., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
  • Structured Embedding Models for Grouped Data Rudolph, M., Ruiz, F., Athey, S., Blei, D., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
  • Recursive partitioning for heterogeneous causal effects PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Athey, S., Imbens, G. 2016; 113 (27): 7353-7360


    In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without "sparsity" assumptions. We propose an "honest" approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the "ground truth" for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7-22%.

    View details for DOI 10.1073/pnas.1510489113

    View details for Web of Science ID 000379021700039

    View details for PubMedID 27382149

    View details for PubMedCentralID PMC4941430

  • A Measure of Robustness to Misspecification AMERICAN ECONOMIC REVIEW Athey, S., Imbens, G. 2015; 105 (5): 476-480
  • Dynamics of Open Source Movements JOURNAL OF ECONOMICS & MANAGEMENT STRATEGY Athey, S., Ellison, G. 2014; 23 (2): 294-316

    View details for DOI 10.1111/jems.12053

    View details for Web of Science ID 000333811500003

  • AN EFFICIENT DYNAMIC MECHANISM ECONOMETRICA Athey, S., Segal, I. 2013; 81 (6): 2463-2485

    View details for DOI 10.3982/ECTA6995

    View details for Web of Science ID 000326878900007

  • Set-Asides and Subsidies in Auctions AMERICAN ECONOMIC JOURNAL-MICROECONOMICS Athey, S., Coey, D., Levin, J. 2013; 5 (1): 1-27

    View details for DOI 10.1257/mic.5.1.1

    View details for Web of Science ID 000314063400001

  • Designing efficient mechanisms for dynamic bilateral trading games 119th Annual Meeting of the American-Economic-Association Athey, S., Segal, I. AMER ECONOMIC ASSOC. 2007: 131–36
  • Identification and inference in nonlinear difference-in-differences models ECONOMETRICA Athey, S., Imbens, G. W. 2006; 74 (2): 431-497
  • The optimal degree of discretion in monetary policy ECONOMETRICA Athey, S., Atkeson, A., Kehoe, P. J. 2005; 73 (5): 1431-1475
  • Collusion and price rigidity REVIEW OF ECONOMIC STUDIES Athey, S., Bagwell, K., Sanchirico, C. 2004; 71 (2): 317-349
  • Identification of standard auction models ECONOMETRICA Athey, S., Haile, P. A. 2002; 70 (6): 2107-2140
  • The impact of information technology on emergency health care outcomes Conference on the Industrial-Organization-of-Medical-Care Athey, S., Stern, S. BLACKWELL PUBLISHING. 2002: 399–432


    We analyze the productivity of information technology in emergency response systems. "Enhanced 911" (E911) is information technology that links caller identification to a location database and so speeds up emergency response. We assess the impact of E911 on health outcomes using Pennsylvania ambulance and hospital records between 1994 and 1996, a period of substantial adoption. We find that as a result of E911 adoption, patient health measured at the time of ambulance arrival improves, suggesting that E911 speeds up emergency response. Further analysis using hospital discharge data shows that E911 reduces mortality and hospital costs.

    View details for Web of Science ID 000179256800004

    View details for PubMedID 12585298

  • Monotone comparative statics under uncertainty QUARTERLY JOURNAL OF ECONOMICS Athey, S. 2002; 117 (1): 187-223
  • Optimal collusion with private information RAND JOURNAL OF ECONOMICS Athey, S., Bagwell, K. 2001; 32 (3): 428-465
  • Organizational design: Decision rights and incentive contracts 113th Annual Meeting of the American-Economics-Association Athey, S., Roberts, J. AMER ECONOMIC ASSOC. 2001: 200–205
  • Information and competition in US forest service timber auctions JOURNAL OF POLITICAL ECONOMY Athey, S., Levin, J. 2001; 109 (2): 375-417