Susan Athey

All Publications

• Economists (and Economics) in Tech Companies JOURNAL OF ECONOMIC PERSPECTIVES Athey, S., Luca, M. 2019; 33 (1): 209–30

  View details for DOI 10.1257/jep.33.1.209

  View details for Web of Science ID 000466839300011

• 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

• 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

  View details for DOI 10.1257/pandp.20181031

  View details for Web of Science ID 000434468600012

• 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

  View details for DOI 10.1287/mnsc.2016.2675

  View details for Web of Science ID 000429494100007

• The value of information in monotone decision problems RESEARCH IN ECONOMICS Athey, S., Levin, J. 2018; 72 (1): 101–16

  View details for DOI 10.1016/j.rie.2017.01.001

  View details for Web of Science ID 000426134400006

• 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

  View details for Web of Science ID 000461823305017

• Stable Prediction across Unknown Environments Kuang, K., Cui, P., Athey, S., Xiong, R., Li, B., ACM ASSOC COMPUTING MACHINERY. 2018: 1617–26

  View details for DOI 10.1145/3219819.3220082

  View details for Web of Science ID 000455346400168

• Exact p-Values for Network Interference JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Athey, S., Eckles, D., Imbens, G. W. 2018; 113 (521): 230–40

  View details for DOI 10.1080/01621459.2016.1241178

  View details for Web of Science ID 000438960500026

• 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

  View details for DOI 10.1080/01621459.2017.1319839

  View details for Web of Science ID 000446710500023

• 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

  View details for Web of Science ID 000452649404086

• 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

  View details for Web of Science ID 000452649400024

• Beyond prediction: Using big data for policy problems. Science (New York, N.Y.) Athey, S. 2017; 355 (6324): 483–85

  Abstract

  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

• 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

  Abstract

  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

  View details for DOI 10.1257/aer.p20151020

  View details for Web of Science ID 000357929400089

• 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

  View details for Web of Science ID 000246986500020

• Identification and inference in nonlinear difference-in-differences models ECONOMETRICA Athey, S., Imbens, G. W. 2006; 74 (2): 431-497

  View details for Web of Science ID 000235876700004

• The optimal degree of discretion in monetary policy ECONOMETRICA Athey, S., Atkeson, A., Kehoe, P. J. 2005; 73 (5): 1431-1475

  View details for Web of Science ID 000231411500002

• Collusion and price rigidity REVIEW OF ECONOMIC STUDIES Athey, S., Bagwell, K., Sanchirico, C. 2004; 71 (2): 317-349

  View details for Web of Science ID 000220632300002

• Identification of standard auction models ECONOMETRICA Athey, S., Haile, P. A. 2002; 70 (6): 2107-2140

  View details for Web of Science ID 000178986600001

• 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

  Abstract

  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

  View details for Web of Science ID 000173476500006

• Optimal collusion with private information RAND JOURNAL OF ECONOMICS Athey, S., Bagwell, K. 2001; 32 (3): 428-465

  View details for Web of Science ID 000172364600003

• 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

  View details for Web of Science ID 000169114600039

• Information and competition in US forest service timber auctions JOURNAL OF POLITICAL ECONOMY Athey, S., Levin, J. 2001; 109 (2): 375-417

  View details for Web of Science ID 000167576100007