
Susan Athey
Economics of Technology Professor, Senior Fellow at the Stanford Institute for Economic Policy Research and Professor, by courtesy, of Economics
Web page: http://athey.people.stanford.edu/
Academic Appointments
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Professor, Economics
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Senior Fellow, Stanford Institute for Economic Policy Research (SIEPR)
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Professor (By courtesy), Economics
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Associate Director, Institute for Human-Centered Artificial Intelligence (HAI)
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Member, Wu Tsai Neurosciences Institute
2020-21 Courses
- Data-Driven Impact
ALP 301 (Spr) - Machine Learning and Causal Inference
ECON 293 (Spr) - Machine Learning and Causal Inference
MGTECON 634 (Spr) -
Independent Studies (9)
- Directed Reading
ECON 139D (Aut, Win, Spr, Sum) - Directed Reading
ECON 239D (Aut, Win, Spr, Sum) - Doctoral Practicum in Research
MGTECON 699 (Aut, Win, Spr, Sum) - Doctoral Practicum in Teaching
MGTECON 698 (Aut, Win, Spr, Sum) - Honors Thesis Research
ECON 199D (Aut, Win, Spr, Sum) - Individual Research
GSBGEN 390 (Aut, Win, Spr) - Ph.D. Research
CME 400 (Aut, Win, Spr, Sum) - PhD Directed Reading
ACCT 691, FINANCE 691, MGTECON 691, MKTG 691, OB 691, OIT 691, POLECON 691 (Aut, Win, Spr) - Practical Training
ECON 299 (Aut, Win, Spr, Sum)
- Directed Reading
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Prior Year Courses
2019-20 Courses
- Data-Driven Impact
ALP 301 (Spr) - Machine Learning and Causal Inference
ECON 293 (Spr) - Machine Learning and Causal Inference
MGTECON 634 (Spr) - Marketplaces for Goods and Services
STRAMGT 529 (Win)
2018-19 Courses
- Cryptocurrency
MGTECON 515 (Spr) - Machine Learning and Causal Inference
ECON 293 (Spr) - Machine Learning and Causal Inference
MGTECON 634 (Spr) - Marketplaces for Goods and Services
STRAMGT 529 (Win) - Using Technology and Market Interventions to Solve Social Problems
GSBGEN 513 (Spr)
2017-18 Courses
- Advertising and Monetization
STRAMGT 518 (Win) - Cryptocurrency
MGTECON 515 (Spr) - Machine Learning and Causal Inference
ECON 293 (Spr) - Machine Learning and Causal Inference
MGTECON 634 (Spr) - Marketplaces for Goods and Services
STRAMGT 529 (Win)
- Data-Driven Impact
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Swarnadip Ghosh, Zhaolei Shi -
Postdoctoral Faculty Sponsor
Mustapha Harb, Emil Palikot, Lisa Simon, Erik Sverdrup, Ruoxuan Xiong -
Doctoral Dissertation Advisor (AC)
Allison Koenecke, Sanath Kumar Krishnamurthy, Ruohan Zhan -
Master's Program Advisor
Kaushik Narasimhan -
Doctoral Dissertation Co-Advisor (AC)
Karthik Rajkumar, Jiaming Zeng -
Doctoral (Program)
Ayush Kanodia -
Postdoctoral Research Mentor
Emil Palikot, Lisa Simon
All Publications
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Market design to accelerate COVID-19 vaccine supply.
Science (New York, N.Y.)
2021
Abstract
Build more capacity, and stretch what we already have.
View details for DOI 10.1126/science.abg0889
View details for PubMedID 33632897
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Association of alpha1-Blocker Receipt With 30-Day Mortality and Risk of Intensive Care Unit Admission Among Adults Hospitalized With Influenza or Pneumonia in Denmark.
JAMA network open
2021; 4 (2): e2037053
Abstract
Importance: Alpha 1-adrenergic receptor blocking agents (alpha1-blockers) have been reported to have protective benefits against hyperinflammation and cytokine storm syndrome, conditions that are associated with mortality in patients with coronavirus disease 2019 and other severe respiratory tract infections. However, studies of the association of alpha1-blockers with outcomes among human participants with respiratory tract infections are scarce.Objective: To examine the association between the receipt of alpha1-blockers and outcomes among adult patients hospitalized with influenza or pneumonia.Design, Setting, and Participants: This population-based cohort study used data from Danish national registries to identify individuals 40 years and older who were hospitalized with influenza or pneumonia between January 1, 2005, and November 30, 2018, with follow-up through December 31, 2018. In the main analyses, patients currently receiving alpha1-blockers were compared with those not receiving alpha1-blockers (defined as patients with no prescription for an alpha1-blocker filled within 365 days before the index date) and those currently receiving 5alpha-reductase inhibitors. Propensity scores were used to address confounding factors and to compute weighted risks, absolute risk differences, and risk ratios. Data were analyzed from April 21 to December 21, 2020.Exposures: Current receipt of alpha1-blockers compared with nonreceipt of alpha1-blockers and with current receipt of 5alpha-reductase inhibitors.Main Outcomes and Measures: Death within 30 days of hospital admission and risk of intensive care unit (ICU) admission.Results: A total of 528 467 adult patients (median age, 75.0 years; interquartile range, 64.4-83.6 years; 273 005 men [51.7%]) were hospitalized with influenza or pneumonia in Denmark between 2005 and 2018. Of those, 21 772 patients (4.1%) were currently receiving alpha1-blockers compared with a population of 22 117 patients not receiving alpha1-blockers who were weighted to the propensity score distribution of those receiving alpha1-blockers. In the propensity score-weighted analyses, patients receiving alpha1-blockers had lower 30-day mortality (15.9%) compared with patients not receiving alpha1-blockers (18.5%), with a corresponding risk difference of -2.7% (95% CI, -3.2% to -2.2%) and a risk ratio (RR) of 0.85 (95% CI, 0.83-0.88). The risk of ICU admission was 7.3% among patients receiving alpha1-blockers and 7.7% among those not receiving alpha1-blockers (risk difference, -0.4% [95% CI, -0.8% to 0%]; RR, 0.95 [95% CI, 0.90-1.00]). A comparison between 18 280 male patients currently receiving alpha1-blockers and 18 228 propensity score-weighted male patients currently receiving 5alpha-reductase inhibitors indicated that those receiving alpha1-blockers had lower 30-day mortality (risk difference, -2.0% [95% CI, -3.4% to -0.6%]; RR, 0.89 [95% CI, 0.82-0.96]) and a similar risk of ICU admission (risk difference, -0.3% [95% CI, -1.4% to 0.7%]; RR, 0.96 [95% CI, 0.83-1.10]).Conclusions and Relevance: This cohort study's findings suggest that the receipt of alpha1-blockers is associated with protective benefits among adult patients hospitalized with influenza or pneumonia.
View details for DOI 10.1001/jamanetworkopen.2020.37053
View details for PubMedID 33566109
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Falling living standards during the COVID-19 crisis: Quantitative evidence from nine developing countries.
Science advances
2021; 7 (6)
Abstract
Despite numerous journalistic accounts, systematic quantitative evidence on economic conditions during the ongoing COVID-19 pandemic remains scarce for most low- and middle-income countries, partly due to limitations of official economic statistics in environments with large informal sectors and subsistence agriculture. We assemble evidence from over 30,000 respondents in 16 original household surveys from nine countries in Africa (Burkina Faso, Ghana, Kenya, Rwanda, Sierra Leone), Asia (Bangladesh, Nepal, Philippines), and Latin America (Colombia). We document declines in employment and income in all settings beginning March 2020. The share of households experiencing an income drop ranges from 8 to 87% (median, 68%). Household coping strategies and government assistance were insufficient to sustain precrisis living standards, resulting in widespread food insecurity and dire economic conditions even 3 months into the crisis. We discuss promising policy responses and speculate about the risk of persistent adverse effects, especially among children and other vulnerable groups.
View details for DOI 10.1126/sciadv.abe0997
View details for PubMedID 33547077
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POLICY LEARNING WITH OBSERVATIONAL DATA
ECONOMETRICA
2021; 89 (1): 133–61
View details for DOI 10.3982/ECTA15732
View details for Web of Science ID 000607743600005
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Local Linear Forests
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
2020
View details for DOI 10.1080/10618600.2020.1831930
View details for Web of Science ID 000588170300001
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Peaches, lemons, and cookies: Designing auction markets with dispersed information
GAMES AND ECONOMIC BEHAVIOR
2020; 124: 454–77
View details for DOI 10.1016/j.geb.2020.09.004
View details for Web of Science ID 000594531700024
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The Allocation of Decision Authority to Human and Artificial Intelligence
AMER ECONOMIC ASSOC. 2020: 80–84
View details for DOI 10.1257/pandp.20201034
View details for Web of Science ID 000534590600014
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SHOPPER: A PROBABILISTIC MODEL OF CONSUMER CHOICE WITH SUBSTITUTES AND COMPLEMENTS
ANNALS OF APPLIED STATISTICS
2020; 14 (1): 1–27
View details for DOI 10.1214/19-AOAS1265
View details for Web of Science ID 000527373000001
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SAMPLING-BASED VERSUS DESIGN-BASED UNCERTAINTY IN REGRESSION ANALYSIS
ECONOMETRICA
2020; 88 (1): 265–96
View details for DOI 10.3982/ECTA12675
View details for Web of Science ID 000534143500008
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Preventing cytokine storm syndrome in COVID-19 using α-1 adrenergic receptor antagonists.
The Journal of clinical investigation
2020
Abstract
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
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Computational social science: Obstacles and opportunities.
Science (New York, N.Y.)
2020; 369 (6507): 1060–62
View details for DOI 10.1126/science.aaz8170
View details for PubMedID 32855329
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Economists (and Economics) in Tech Companies
JOURNAL OF ECONOMIC PERSPECTIVES
2019; 33 (1): 209–30
View details for DOI 10.1257/jep.33.1.209
View details for Web of Science ID 000466839300011
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Comment on: "The Blessings of Multiple Causes" by Yixin Wang and David M. Blei
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
2019; 114 (528): 1602–4
View details for DOI 10.1080/01621459.2019.1691008
View details for Web of Science ID 000505405600014
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GENERALIZED RANDOM FORESTS
ANNALS OF STATISTICS
2019; 47 (2): 1148–78
View details for DOI 10.1214/18-AOS1709
View details for Web of Science ID 000455476800018
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Balanced Linear Contextual Bandits
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2019: 3445–53
View details for Web of Science ID 000485292603057
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Machine Learning Methods That Economists Should Know About
ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019
2019; 11: 685–725
View details for DOI 10.1146/annurev-economics-080217-053433
View details for Web of Science ID 000483866000026
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Approximate residual balancing: debiased inference of average treatment effects in high dimensions
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
2018; 80 (4): 597–623
View details for DOI 10.1111/rssb.12268
View details for Web of Science ID 000442217900001
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Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data
AMER ECONOMIC ASSOC. 2018: 64–67
View details for DOI 10.1257/pandp.20181031
View details for Web of Science ID 000434468600012
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The Impact of Consumer Multi-homing on Advertising Markets and Media Competition
MANAGEMENT SCIENCE
2018; 64 (4): 1574–90
View details for DOI 10.1287/mnsc.2016.2675
View details for Web of Science ID 000429494100007
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The value of information in monotone decision problems
RESEARCH IN ECONOMICS
2018; 72 (1): 101–16
View details for DOI 10.1016/j.rie.2017.01.001
View details for Web of Science ID 000426134400006
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Exact p-Values for Network Interference
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
2018; 113 (521): 230–40
View details for DOI 10.1080/01621459.2016.1241178
View details for Web of Science ID 000438960500026
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Stable Prediction across Unknown Environments
ASSOC COMPUTING MACHINERY. 2018: 1617–26
View details for DOI 10.1145/3219819.3220082
View details for Web of Science ID 000455346400168
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Learning in Games with Lossy Feedback
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
View details for Web of Science ID 000461823305017
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
2018; 113 (523): 1228–42
View details for DOI 10.1080/01621459.2017.1319839
View details for Web of Science ID 000446710500023
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Beyond prediction: Using big data for policy problems.
Science (New York, N.Y.)
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
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Context Selection for Embedding Models
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
View details for Web of Science ID 000452649404086
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Structured Embedding Models for Grouped Data
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
View details for Web of Science ID 000452649400024
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Recursive partitioning for heterogeneous causal effects
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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
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A Measure of Robustness to Misspecification
AMERICAN ECONOMIC REVIEW
2015; 105 (5): 476-480
View details for DOI 10.1257/aer.p20151020
View details for Web of Science ID 000357929400089
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Dynamics of Open Source Movements
JOURNAL OF ECONOMICS & MANAGEMENT STRATEGY
2014; 23 (2): 294-316
View details for DOI 10.1111/jems.12053
View details for Web of Science ID 000333811500003
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AN EFFICIENT DYNAMIC MECHANISM
ECONOMETRICA
2013; 81 (6): 2463-2485
View details for DOI 10.3982/ECTA6995
View details for Web of Science ID 000326878900007
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Set-Asides and Subsidies in Auctions
AMERICAN ECONOMIC JOURNAL-MICROECONOMICS
2013; 5 (1): 1-27
View details for DOI 10.1257/mic.5.1.1
View details for Web of Science ID 000314063400001
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Designing efficient mechanisms for dynamic bilateral trading games
119th Annual Meeting of the American-Economic-Association
AMER ECONOMIC ASSOC. 2007: 131–36
View details for Web of Science ID 000246986500020
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Identification and inference in nonlinear difference-in-differences models
ECONOMETRICA
2006; 74 (2): 431-497
View details for Web of Science ID 000235876700004
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The optimal degree of discretion in monetary policy
ECONOMETRICA
2005; 73 (5): 1431-1475
View details for Web of Science ID 000231411500002
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Collusion and price rigidity
REVIEW OF ECONOMIC STUDIES
2004; 71 (2): 317-349
View details for Web of Science ID 000220632300002
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Identification of standard auction models
ECONOMETRICA
2002; 70 (6): 2107-2140
View details for Web of Science ID 000178986600001
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The impact of information technology on emergency health care outcomes
Conference on the Industrial-Organization-of-Medical-Care
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
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Monotone comparative statics under uncertainty
QUARTERLY JOURNAL OF ECONOMICS
2002; 117 (1): 187-223
View details for Web of Science ID 000173476500006
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Optimal collusion with private information
RAND JOURNAL OF ECONOMICS
2001; 32 (3): 428-465
View details for Web of Science ID 000172364600003
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Organizational design: Decision rights and incentive contracts
113th Annual Meeting of the American-Economics-Association
AMER ECONOMIC ASSOC. 2001: 200–205
View details for Web of Science ID 000169114600039
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Information and competition in US forest service timber auctions
JOURNAL OF POLITICAL ECONOMY
2001; 109 (2): 375-417
View details for Web of Science ID 000167576100007