Susan Athey

All Publications

• Estimating wage disparities using foundation models. Proceedings of the National Academy of Sciences of the United States of America Vafa, K., Athey, S., Blei, D. M. 2025; 122 (22): e2427298122

  Abstract

  The rise of foundation models marks a paradigm shift in machine learning: instead of training specialized models from scratch, foundation models are trained on massive datasets before being adjusted or fine-tuned to make predictions on smaller datasets. Initially developed for text, foundation models have also excelled at making predictions about social science data. However, while many estimation problems in the social sciences use prediction as an intermediate step, they ultimately require different criteria for success. In this paper, we develop methods for fine-tuning foundation models to perform these estimation problems. We first characterize an omitted variable bias that can arise when a foundation model is fine-tuned in the standard way: to minimize predictive error. We then provide a set of conditions for fine-tuning under which estimates derived from a foundation model are [Formula: see text]-consistent. Based on this theory, we develop fine-tuning algorithms that empirically mitigate this omitted variable bias. To demonstrate our ideas, we study gender wage gap estimation. Classical methods for estimating the adjusted wage gap employ simple predictive models of wages, which can induce omitted variable bias because they condition on coarse summaries of career history. Instead, we use a custom-built foundation model, capturing a richer representation of career history. Using data from the Panel Study of Income Dynamics, we find that career history explains more of the gender wage gap than standard econometric models can measure, and we identify elements of career history that are omitted by standard models but are important for explaining the gap.

  View details for DOI 10.1073/pnas.2427298122

  View details for PubMedID 40445754

• Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal JOURNAL OF ECONOMETRICS Athey, S., Keleher, N., Spiess, J. 2025; 249

  View details for DOI 10.1016/j.jeconom.2024.105945

  View details for Web of Science ID 001495893600014

• Presidential Address: The Economist as Designer in the Innovation Process for Socially Impactful Digital Products AMERICAN ECONOMIC REVIEW Athey, S. 2025; 115 (4): 1059-1099

  View details for DOI 10.1257/aer.115.4.1059

  View details for Web of Science ID 001468657400001

• On synthetic difference-in-differences and related estimation methods in Stata STATA JOURNAL Clarke, D., Pailanir, D., Athey, S., Imbens, G. 2024; 24 (4): 557-598

  View details for DOI 10.1177/1536867X241297914

  View details for Web of Science ID 001386719400008

• Qini Curves for Multi-Armed Treatment Rules JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS Sverdrup, E., Wu, H., Athey, S., Wager, S. 2024

  View details for DOI 10.1080/10618600.2024.2418820

  View details for Web of Science ID 001373394200001

• The Year in Review: Economics at the Antitrust Division 2023-2024 REVIEW OF INDUSTRIAL ORGANIZATION Athey, S., Gross, A., Marinescu, I., Shanefelter, J. 2024

  View details for DOI 10.1007/s11151-024-09998-2

  View details for Web of Science ID 001347278700001

• Health insurance might be more beneficial to health than average effects suggest. BMJ (Clinical research ed.) Inoue, K., Athey, S., Baicker, K., Tsugawa, Y. 2024; 386: q2090

  View details for DOI 10.1136/bmj.q2090

  View details for PubMedID 39317388

• Heterogeneous effects of Medicaid coverage on cardiovascular risk factors: secondary analysis of randomized controlled trial. BMJ (Clinical research ed.) Inoue, K., Athey, S., Baicker, K., Tsugawa, Y. 2024; 386: e079377

  Abstract

  To investigate whether health insurance generated improvements in cardiovascular risk factors (blood pressure and hemoglobin A1c (HbA1c) levels) for identifiable subpopulations, and using machine learning to identify characteristics of people predicted to benefit highly.Secondary analysis of randomized controlled trial.Medicaid insurance coverage in 2008 for adults on low incomes (defined as lower than the federal-defined poverty line) in Oregon who were uninsured.12 134 participants from the Oregon Health Insurance Experiment with in-person data for health outcomes for both treatment and control groups.Health insurance (Medicaid) coverage.The conditional local average treatment effects of Medicaid coverage on systolic blood pressure and HbA1c using a machine learning causal forest algorithm (with instrumental variables). Characteristics of individuals with positive predicted benefits of Medicaid coverage based on the algorithm were compared with the characteristics of others. The effect of Medicaid coverage was calculated on blood pressure and HbA1c among individuals with high predicted benefits.In the in-person interview survey, mean systolic blood pressure was 119 (standard deviation 17) mm Hg and mean HbA1c concentrations was 5.3% (standard deviation 0.6%). Our causal forest model showed heterogeneity in the effect of Medicaid coverage on systolic blood pressure and HbA1c. Individuals with lower baseline healthcare charges, for example, had higher predicted benefits from gaining Medicaid coverage. Medicaid coverage significantly lowered systolic blood pressure (-4.96 mm Hg (95% confidence interval -7.80 to -2.48)) for people predicted to benefit highly. HbA1c was also significantly reduced by Medicaid coverage for people with high predicted benefits, but the size was not clinically meaningful (-0.12% (-0.25% to -0.01%)).Although Medicaid coverage did not improve cardiovascular risk factors on average, substantial heterogeneity was noted in the effects within that population. Individuals with high predicted benefits were more likely to have no or low prior healthcare charges, for example. Our findings suggest that Medicaid coverage leads to improved cardiovascular risk factors for some, particularly for blood pressure, although those benefits may be diluted by individuals who did not experience benefits.

  View details for DOI 10.1136/bmj-2024-079377

  View details for PubMedID 39313257

  View details for PubMedCentralID PMC11417663

• Policy Learning with Adaptively Collected Data MANAGEMENT SCIENCE Zhan, R., Ren, Z., Athey, S., Zhou, Z. 2024; 70 (8): 5270-5297

  View details for DOI 10.1287/mnsc.2023.4921

  View details for Web of Science ID 001304602200006

• Battling the coronavirus 'infodemic' among social media users in Kenya and Nigeria. Nature human behaviour Offer-Westort, M., Rosenzweig, L. R., Athey, S. 2024

  Abstract

  How can we induce social media users to be discerning when sharing information during a pandemic? An experiment on Facebook Messenger with users from Kenya (n=7,498) and Nigeria (n=7,794) tested interventions designed to decrease intentions to share COVID-19 misinformation without decreasing intentions to share factual posts. The initial stage of the study incorporated: (1) a factorial design with 40 intervention combinations; and (2) a contextual adaptive design, increasing the probability of assignment to treatments that worked better for previous subjects with similar characteristics. The second stage evaluated the best-performing treatments and a targeted treatment assignment policy estimated from the data. We precisely estimate null effects from warning flags and related article suggestions, tactics used by social media platforms. However, nudges to consider the accuracy of information reduced misinformation sharing relative to control by 4.9% (estimate=-2.3percentage points, 95% CI=[-4.2, -0.35]). Such low-cost scalable interventions may improve the quality of information circulating online.

  View details for DOI 10.1038/s41562-023-01810-7

  View details for PubMedID 38499773

• Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations☆ JOURNAL OF ECONOMETRICS Athey, S., Imbens, G. W., Metzger, J., Munro, E. 2024; 240 (2)

  View details for DOI 10.1016/j.jeconom.2020.09.013

  View details for Web of Science ID 001222944300001

• Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective Krishnamurthy, S., Propp, A., Athey, S., Dasgupta, S., Mandt, S., Li, Y. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

  View details for Web of Science ID 001286500300003

• Optimal Experimental Design for Staggered Rollouts MANAGEMENT SCIENCE Xiong, R., Athey, S., Bayati, M., Imbens, G. 2023

  View details for DOI 10.1287/mnsc.2023.4928

  View details for Web of Science ID 001126299600001

• Can personalized digital counseling improve consumer search for modern contraceptive methods? Science advances Athey, S., Bergstrom, K., Hadad, V., Jamison, J. C., Ozler, B., Parisotto, L., Sama, J. D. 2023; 9 (40): eadg4420

  Abstract

  This paper analyzes a randomized controlled trial of a personalized digital counseling intervention addressing informational constraints and choice architecture, cross-randomized with discounts for long-acting reversible contraceptives (LARCs), such as intrauterine devices (IUDs). The counseling intervention encourages shared decision-making (SDM) using a tablet-based app, which provides a tailored ranking of modern methods to each client according to their elicited needs and preferences. Take-up of LARCs in the status quo regime at full price was 11%, which increased to 28% with discounts. SDM roughly tripled the share of clients adopting a LARC at full price to 35%, and discounts had no incremental impact in this group. Neither intervention affected the take-up of short-acting methods, such as the pill. Consistent with theoretical models of consumer search, SDM clients discussed more methods in depth, which led to higher adoption rates for second- or lower-ranked LARCs. Our findings suggest that low-cost individualized recommendations can potentially be as effective in increasing unfamiliar technology adoption as providing large subsidies.

  View details for DOI 10.1126/sciadv.adg4420

  View details for PubMedID 37801502

• Federated causal inference in heterogeneous observational data. Statistics in medicine Xiong, R., Koenecke, A., Powell, M., Shen, Z., Vogelstein, J. T., Athey, S. 2023

  Abstract

  We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individual-level data cannot be shared across sites; the sites may also have heterogeneous populations and treatment assignment mechanisms. Motivated by these considerations, we develop federated methods to draw inferences on the average treatment effects of combined data across sites. Our methods first compute summary statistics locally using propensity scores and then aggregate these statistics across sites to obtain point and variance estimators of average treatment effects. We show that these estimators are consistent and asymptotically normal. To achieve these asymptotic properties, we find that the aggregation schemes need to account for the heterogeneity in treatment assignments and in outcomes across sites. We demonstrate the validity of our federated methods through a comparative study of two large medical claims databases.

  View details for DOI 10.1002/sim.9868

  View details for PubMedID 37553084

• Semi-parametric estimation of treatment effects in randomised experiments JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY Athey, S., Bickel, P. J., Chen, A., Imbens, G. W., Pollmann, M. 2023

  View details for DOI 10.1093/jrsssb/qkad072

  View details for Web of Science ID 001033152400001

• Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management. International journal of epidemiology Inoue, K., Athey, S., Tsugawa, Y. 2023

  Abstract

  BACKGROUND: In medicine, clinicians treat individuals under an implicit assumption that high-risk patients would benefit most from the treatment ('high-risk approach'). However, treating individuals with the highest estimated benefit using a novel machine-learning method ('high-benefit approach') may improve population health outcomes.METHODS: This study included 10 672 participants who were randomized to systolic blood pressure (SBP) target of either <120 mmHg (intensive treatment) or <140 mmHg (standard treatment) from two randomized controlled trials (Systolic Blood Pressure Intervention Trial, and Action to Control Cardiovascular Risk in Diabetes Blood Pressure). We applied the machine-learning causal forest to develop a prediction model of individualized treatment effect (ITE) of intensive SBP control on the reduction in cardiovascular outcomes at 3 years. We then compared the performance of high-benefit approach (treating individuals with ITE>0) versus the high-risk approach (treating individuals with SBP≥130mmHg). Using transportability formula, we also estimated the effect of these approaches among 14 575 US adults from National Health and Nutrition Examination Surveys (NHANES) 1999-2018.RESULTS: We found that 78.9% of individuals with SBP ≥130mmHg benefited from the intensive SBP control. The high-benefit approach outperformed the high-risk approach [average treatment effect (95% CI), +9.36 (8.33-10.44) vs +1.65 (0.36-2.84) percentage point; difference between these two approaches, +7.71 (6.79-8.67) percentage points, P-value<0.001]. The results were consistent when we transported the results to the NHANES data.CONCLUSIONS: The machine-learning-based high-benefit approach outperformed the high-risk approach with a larger treatment effect. These findings indicate that the high-benefit approach has the potential to maximize the effectiveness of treatment rather than the conventional high-risk approach, which needs to be validated in future research.

  View details for DOI 10.1093/ije/dyad037

  View details for PubMedID 37013846

• Digital public health interventions at scale: The impact of social media advertising on beliefs and outcomes related to COVID vaccines. Proceedings of the National Academy of Sciences of the United States of America Athey, S., Grabarz, K., Luca, M., Wernerfelt, N. 2023; 120 (5): e2208110120

  Abstract

  Public health organizations increasingly use social media advertising campaigns in pursuit of public health goals. In this paper, we evaluate the impact of about $40 million of social media advertisements that were run and experimentally tested on Facebook and Instagram, aimed at increasing COVID-19 vaccination rates in the first year of the vaccine roll-out. The 819 randomized experiments in our sample were run by 174 different public health organizations and collectively reached 2.1 billion individuals in 15 languages. We find that these campaigns are, on average, effective at influencing self-reported beliefs-shifting opinions close to 1% at baseline with a cost per influenced person of about $3.41. Combining this result with an estimate of the relationship between survey outcomes and vaccination rates derived from observational data yields an estimated cost per additional vaccination of about $5.68. There is further evidence that campaigns are especially effective at influencing users' knowledge of how to get vaccines. Our results represent, to the best of our knowledge, the largest set of online public health interventions analyzed to date.

  View details for DOI 10.1073/pnas.2208110120

  View details for PubMedID 36701366

• Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization Krishnamurthy, S., Zhan, R., Athey, S., Brunskill, E., Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

  View details for Web of Science ID 001228825100005

• Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles Carranza, A., Krishnamurthy, S., Athey, S., Ruiz, F., Dy, J., VanDeMeent, J. W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

  View details for Web of Science ID 001298469301011

• Expanding capacity for vaccines against Covid-19 and future pandemics: a review of economic issues OXFORD REVIEW OF ECONOMIC POLICY Athey, S., Castillo, J., Chaudhuri, E., Kremer, M., Gomes, A., Snyder, C. M. 2022; 38 (4): 742-770

  View details for DOI 10.1093/oxrep/grac037

  View details for Web of Science ID 000898268700002

• When Should You Adjust Standard Errors for Clustering?* QUARTERLY JOURNAL OF ECONOMICS Abadie, A., Athey, S., Imbens, G. W., Wooldridge, J. M. 2022

  View details for DOI 10.1093/qje/qjac038

  View details for Web of Science ID 000885663500001

• Offline Multi-Action Policy Learning: Generalization and Optimization OPERATIONS RESEARCH Zhou, Z., Athey, S., Wager, S. 2022

  View details for DOI 10.1287/opre.2022.2271

  View details for Web of Science ID 000809634500001

• Uncovering interpretable potential confounders in electronic medical records. Nature communications Zeng, J., Gensheimer, M. F., Rubin, D. L., Athey, S., Shachter, R. D. 2022; 13 (1): 1014

  Abstract

  Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We explore how unstructured clinical text can be used to reduce selection bias and improve medical practice. We develop a framework based on natural language processing to uncover interpretable potential confounders from text. We validate our method by comparing the estimated hazard ratio (HR) with and without the confounders against established RCTs. We apply our method to four cohorts built from localized prostate and lung cancer datasets from the Stanford Cancer Institute and show that our method shifts the HR estimate towards the RCT results. The uncovered terms can also be interpreted by oncologists for clinical insights. We present this proof-of-concept study to enable more credible causal inference using observational data, uncover meaningful insights from clinical text, and inform high-stakes medical decisions.

  View details for DOI 10.1038/s41467-022-28546-8

  View details for PubMedID 35197467

• Stable learning establishes some common ground between causal inference and machine learning NATURE MACHINE INTELLIGENCE Cui, P., Athey, S. 2022; 4 (2): 110-115

  View details for DOI 10.1038/s42256-022-00445-z

  View details for Web of Science ID 000760318100006

• Design-based analysis in Difference-In-Differences settings with staggered adoption JOURNAL OF ECONOMETRICS Athey, S., Imbens, G. W. 2022; 226 (1): 62-79

  View details for DOI 10.1016/j.jeconom.2020.10.012

  View details for Web of Science ID 000729637300004

• Counterfactual inference for consumer choice across many product categories (Jun, 10.1007/s11129-021-09241-2, 2021) QME-QUANTITATIVE MARKETING AND ECONOMICS Donnelly, R., Ruiz, F. R., Blei, D., Athey, S. 2021

  View details for DOI 10.1007/s11129-021-09245-y

  View details for Web of Science ID 000734346400001

• Synthetic Difference-in-Differences AMERICAN ECONOMIC REVIEW Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., Wager, S. 2021; 111 (12): 4088-4118

  View details for DOI 10.1257/aer.20190159

  View details for Web of Science ID 000725477700008

• Counterfactual inference for consumer choice across many product categories QME-QUANTITATIVE MARKETING AND ECONOMICS Donnelly, R., Ruiz, F. R., Blei, D., Athey, S. 2021

  View details for DOI 10.1007/s11129-021-09241-2

  View details for Web of Science ID 000719728000001

• Estimating experienced racial segregation in US cities using large-scale GPS data. Proceedings of the National Academy of Sciences of the United States of America Athey, S., Ferguson, B., Gentzkow, M., Schmidt, T. 2021; 118 (46)

  Abstract

  We estimate a measure of segregation, experienced isolation, that captures individuals' exposure to diverse others in the places they visit over the course of their days. Using Global Positioning System (GPS) data collected from smartphones, we measure experienced isolation by race. We find that the isolation individuals experience is substantially lower than standard residential isolation measures would suggest but that experienced isolation and residential isolation are highly correlated across cities. Experienced isolation is lower relative to residential isolation in denser, wealthier, more educated cities with high levels of public transit use and is also negatively correlated with income mobility.

  View details for DOI 10.1073/pnas.2026160118

  View details for PubMedID 34764221

• Integrating explanation and prediction in computational social science. Nature Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., Vespignani, A., Yarkoni, T. 2021

  Abstract

  Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions-the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes-and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.

  View details for DOI 10.1038/s41586-021-03659-0

  View details for PubMedID 34194044

• Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection. eLife Koenecke, A., Powell, M., Xiong, R., Shen, Z., Fischer, N., Huq, S., Khalafallah, A. M., Trevisan, M., Sparen, P., Carrero, J. J., Nishimura, A., Caffo, B., Stuart, E. A., Bai, R., Staedtke, V., Thomas, D. L., Papadopoulos, N., Kinzler, K. W., Vogelstein, B., Zhou, S., Bettegowda, C., Konig, M. F., Mensh, B. D., Vogelstein, J. T., Athey, S. 2021; 10

  Abstract

  In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation, which can lead to acute respiratory distress syndrome, multi-organ failure, and death. We previously demonstrated that alpha-1 adrenergic receptor (⍺1-AR) antagonists can prevent hyperinflammation and death in mice. Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n = 18,547) and three cohorts with pneumonia (n = 400,907). Federated across two ARD cohorts, we find that patients exposed to ⍺1-AR antagonists, as compared to unexposed patients, had a 34% relative risk reduction for mechanical ventilation and death (OR = 0.70, p = 0.021). We replicated these methods on three pneumonia cohorts, all with similar effects on both outcomes. All results were robust to sensitivity analyses. These results highlight the urgent need for prospective trials testing whether prophylactic use of ⍺1-AR antagonists ameliorates lower respiratory tract infection-associated hyperinflammation and death, as observed in COVID-19.

  View details for DOI 10.7554/eLife.61700

  View details for PubMedID 34114951

• PRIVATE AND SOCIAL RETURNS TO R&D AND VACCINE DEVELOPMENT Preparing for a Pandemic: Accelerating Vaccine Availability Ahuja, A., Athey, S., Baker, A., Budish, E., Castillo, J., Glennerster, R., Kominers, S., Kremer, M., Lee, J., Prendergast, C., Snyder, C. M., Tabarrok, A., Tan, B., Wiecek, W. AMER ECONOMIC ASSOC. 2021: 331-335

  View details for DOI 10.1257/pandp.20211103

  View details for Web of Science ID 000655908100063

• Matrix Completion Methods for Causal Panel Data Models JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Athey, S., Bayati, M., Doudchenko, N., Imbens, G., Khosravi, K. 2021

  View details for DOI 10.1080/01621459.2021.1891924

  View details for Web of Science ID 000648806000001

• Confidence intervals for policy evaluation in adaptive experiments. Proceedings of the National Academy of Sciences of the United States of America Hadad, V., Hirshberg, D. A., Zhan, R., Wager, S., Athey, S. 2021; 118 (15)

  Abstract

  Adaptive experimental designs can dramatically improve efficiency in randomized trials. But with adaptively collected data, common estimators based on sample means and inverse propensity-weighted means can be biased or heavy-tailed. This poses statistical challenges, in particular when the experimenter would like to test hypotheses about parameters that were not targeted by the data-collection mechanism. In this paper, we present a class of test statistics that can handle these challenges. Our approach is to adaptively reweight the terms of an augmented inverse propensity-weighting estimator to control the contribution of each term to the estimator's variance. This scheme reduces overall variance and yields an asymptotically normal test statistic. We validate the accuracy of the resulting estimates and their CIs in numerical experiments and show that our methods compare favorably to existing alternatives in terms of mean squared error, coverage, and CI size.

  View details for DOI 10.1073/pnas.2014602118

  View details for PubMedID 33876748

• Market design to accelerate COVID-19 vaccine supply. Science (New York, N.Y.) Castillo, J. C., Ahuja, A., Athey, S., Baker, A., Budish, E., Chipty, T., Glennerster, R., Kominers, S. D., Kremer, M., Larson, G., Lee, J., Prendergast, C., Snyder, C. M., Tabarrok, A., Tan, B. J., Wiecek, W. 2021

  Abstract

  Build more capacity, and stretch what we already have.

  View details for DOI 10.1126/science.abg0889

  View details for PubMedID 33632897

• 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 Thomsen, R. W., Christiansen, C. F., Heide-Jorgensen, U., Vogelstein, J. T., Vogelstein, B., Bettegowda, C., Tamang, S., Athey, S., Sorensen, H. T. 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

• Falling living standards during the COVID-19 crisis: Quantitative evidence from nine developing countries. Science advances Egger, D., Miguel, E., Warren, S. S., Shenoy, A., Collins, E., Karlan, D., Parkerson, D., Mobarak, A. M., Fink, G., Udry, C., Walker, M., Haushofer, J., Larreboure, M., Athey, S., Lopez-Pena, P., Benhachmi, S., Humphreys, M., Lowe, L., Meriggi, N. F., Wabwire, A., Davis, C. A., Pape, U. J., Graff, T., Voors, M., Nekesa, C., Vernot, C. 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

• POLICY LEARNING WITH OBSERVATIONAL DATA ECONOMETRICA Athey, S., Wager, S. 2021; 89 (1): 133–61

  View details for DOI 10.3982/ECTA15732

  View details for Web of Science ID 000607743600005

• Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits Zhan, R., Hadad, V., Hirshberg, D. A., Athey, S., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2021: 2125-2135

  View details for DOI 10.1145/3447548.3467456

  View details for Web of Science ID 000749556802017

• Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles Krishnamurthy, S., Hadad, V., Athey, S., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

  View details for Web of Science ID 000683104605076

• Tractable contextual bandits beyond realizability Krishnamurthy, S., Hadad, V., Athey, S., Banerjee, A., Fukumizu, K. MICROTOME PUBLISHING. 2021

  View details for Web of Science ID 000659893801072

• The Association Between Alpha-1 Adrenergic Receptor Antagonists and In-Hospital Mortality From COVID-19. Frontiers in medicine Rose, L., Graham, L., Koenecke, A., Powell, M., Xiong, R., Shen, Z., Mench, B., Kinzler, K. W., Bettegowda, C., Vogelstein, B., Athey, S., Vogelstein, J. T., Konig, M. F., Wagner, T. H. 2021; 8: 637647

  Abstract

  Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed, and pre-clinical data suggest alpha-1 adrenergic receptor antagonists (alpha1-AR antagonists) may be effective in reducing mortality related to hyperinflammation independent of etiology. Using a retrospective cohort design with patients in the Department of Veterans Affairs healthcare system, we use doubly robust regression and matching to estimate the association between baseline use of alpha1-AR antagonists and likelihood of death due to COVID-19 during hospitalization. Having an active prescription for any alpha1-AR antagonist (tamsulosin, silodosin, prazosin, terazosin, doxazosin, or alfuzosin) at the time of admission had a significant negative association with in-hospital mortality (relative risk reduction 18%; odds ratio 0.73; 95% CI 0.63-0.85; p ≤ 0.001) and death within 28 days of admission (relative risk reduction 17%; odds ratio 0.74; 95% CI 0.65-0.84; p ≤ 0.001). In a subset of patients on doxazosin specifically, an inhibitor of all three alpha-1 adrenergic receptors, we observed a relative risk reduction for death of 74% (odds ratio 0.23; 95% CI 0.03-0.94; p = 0.028) compared to matched controls not on any alpha1-AR antagonist at the time of admission. These findings suggest that use of alpha1-AR antagonists may reduce mortality in COVID-19, supporting the need for randomized, placebo-controlled clinical trials in patients with early symptomatic infection.

  View details for DOI 10.3389/fmed.2021.637647

  View details for PubMedID 33869251

• Ten Rules for Conducting Retrospective Pharmacoepidemiological Analyses: Example COVID-19 Study. Frontiers in pharmacology Powell, M., Koenecke, A., Byrd, J. B., Nishimura, A., Konig, M. F., Xiong, R., Mahmood, S., Mucaj, V., Bettegowda, C., Rose, L., Tamang, S., Sacarny, A., Caffo, B., Athey, S., Stuart, E. A., Vogelstein, J. T. 2021; 12: 700776

  Abstract

  Since the beginning of the COVID-19 pandemic, pharmaceutical treatment hypotheses have abounded, each requiring careful evaluation. A randomized controlled trial generally provides the most credible evaluation of a treatment, but the efficiency and effectiveness of the trial depend on the existing evidence supporting the treatment. The researcher must therefore compile a body of evidence justifying the use of time and resources to further investigate a treatment hypothesis in a trial. An observational study can provide this evidence, but the lack of randomized exposure and the researcher's inability to control treatment administration and data collection introduce significant challenges. A proper analysis of observational health care data thus requires contributions from experts in a diverse set of topics ranging from epidemiology and causal analysis to relevant medical specialties and data sources. Here we summarize these contributions as 10 rules that serve as an end-to-end introduction to retrospective pharmacoepidemiological analyses of observational health care data using a running example of a hypothetical COVID-19 study. A detailed supplement presents a practical how-to guide for following each rule. When carefully designed and properly executed, a retrospective pharmacoepidemiological analysis framed around these rules will inform the decisions of whether and how to investigate a treatment hypothesis in a randomized controlled trial. This work has important implications for any future pandemic by prescribing what we can and should do while the world waits for global vaccine distribution.

  View details for DOI 10.3389/fphar.2021.700776

  View details for PubMedID 34393782

• Local Linear Forests JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS Friedberg, R., Tibshirani, J., Athey, S., Wager, S. 2020

  View details for DOI 10.1080/10618600.2020.1831930

  View details for Web of Science ID 000588170300001

• Peaches, lemons, and cookies: Designing auction markets with dispersed information GAMES AND ECONOMIC BEHAVIOR Abraham, I., Athey, S., Babaioff, M., Grubb, M. D. 2020; 124: 454–77

  View details for DOI 10.1016/j.geb.2020.09.004

  View details for Web of Science ID 000594531700024

• The Allocation of Decision Authority to Human and Artificial Intelligence Athey, S. C., Bryan, K. A., Gans, J. S. AMER ECONOMIC ASSOC. 2020: 80–84

  View details for DOI 10.1257/pandp.20201034

  View details for Web of Science ID 000534590600014

• SHOPPER: A PROBABILISTIC MODEL OF CONSUMER CHOICE WITH SUBSTITUTES AND COMPLEMENTS ANNALS OF APPLIED STATISTICS Ruiz, F. R., Athey, S., Blei, D. M. 2020; 14 (1): 1–27

  View details for DOI 10.1214/19-AOAS1265

  View details for Web of Science ID 000527373000001

• The Association Between Alpha-1 Adrenergic Receptor Antagonists and In-Hospital Mortality from COVID-19. medRxiv : the preprint server for health sciences Rose, L. n., Graham, L. n., Koenecke, A. n., Powell, M. n., Xiong, R. n., Shen, Z. n., Kinzler, K. W., Bettegowda, C. n., Vogelstein, B. n., Athey, S. n., Vogelstein, J. T., Konig, M. F., Wagner, T. H. 2020

  Abstract

  Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed, and preclinical data suggest alpha-1 adrenergic receptor antagonists (α 1 -AR antagonists) may be effective in reducing mortality related to hyperinflammation. Using a retrospective cohort design with patients in the Department of Veterans Affairs healthcare system, we use doubly robust regression and matching to estimate the association between use of α 1 -AR antagonists at time of hospitalization and likelihood of death due to COVID-19 during an inpatient stay. Having an active prescription for an α 1 -AR antagonist (tamsulosin, silodosin, prazosin, terazosin, doxazosin, or alfuzosin) at the time of admission had a significant negative association with in-hospital mortality (relative risk reduction 14%; odds ratio 0.75; 95% CI 0.66 to 0.86; p ≤ 0.001). These effects were also found in an expanded cohort of suspected COVID-19 patients, supporting the need for clinical trials.

  View details for DOI 10.1101/2020.12.18.20248346

  View details for PubMedID 33398294

  View details for PubMedCentralID PMC7781337

• Stable Prediction with Model Misspecification and Agnostic Distribution Shift Kuang, K., Xiong, R., Cui, P., Athey, S., Li, B., Assoc Advancement Artificial Intelligence ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2020: 4485-4492

  View details for Web of Science ID 000667722804068

• 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. n., Bai, R. Y., Thomas, D. L., Fischer, N. M., Huq, S. n., Khalafallah, A. M., Koenecke, A. n., Xiong, R. n., Mensh, B. n., Papadopoulos, N. n., Kinzler, K. W., Vogelstein, B. n., Vogelstein, J. T., Athey, S. n., Zhou, S. n., Bettegowda, C. n. 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

• Computational social science: Obstacles and opportunities. Science (New York, N.Y.) Lazer, D. M., Pentland, A. n., Watts, D. J., Aral, S. n., Athey, S. n., Contractor, N. n., Freelon, D. n., Gonzalez-Bailon, S. n., King, G. n., Margetts, H. n., Nelson, A. n., Salganik, M. J., Strohmaier, M. n., Vespignani, A. n., Wagner, C. n. 2020; 369 (6507): 1060–62

  View details for DOI 10.1126/science.aaz8170

  View details for PubMedID 32855329

• SAMPLING-BASED VERSUS DESIGN-BASED UNCERTAINTY IN REGRESSION ANALYSIS ECONOMETRICA Abadie, A., Athey, S., Imbens, G. W., Wooldridge, J. M. 2020; 88 (1): 265–96

  View details for DOI 10.3982/ECTA12675

  View details for Web of Science ID 000534143500008

• 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

• 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

  View details for DOI 10.1080/01621459.2019.1691008

  View details for Web of Science ID 000505405600014

• 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

• Balanced Linear Contextual Bandits Dimakopoulou, M., Zhou, Z., Athey, S., Imbens, G., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2019: 3445–53

  View details for Web of Science ID 000485292603057

• 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

  View details for DOI 10.1146/annurev-economics-080217-053433

  View details for Web of Science ID 000483866000026

• 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

• 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

• 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

• Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges AMERICAN ECONOMIC REVIEW Athey, S., Imbens, G., Pham, T., Wager, S. 2017; 107 (5): 278-281

  View details for DOI 10.1257/aer.p20171042

  View details for Web of Science ID 000402551700054

• Yuliy Sannikov: Winner of the 2016 Clark Medal JOURNAL OF ECONOMIC PERSPECTIVES Athey, S., Skrzypacz, A. 2017; 31 (2): 237-255

  View details for DOI 10.1257/jep.31.2.237

  View details for Web of Science ID 000403753100011

• The State of Applied Econometrics: Causality and Policy Evaluation JOURNAL OF ECONOMIC PERSPECTIVES Athey, S., Imbens, G. W. 2017; 31 (2): 3-32

  View details for DOI 10.1257/jep.31.2.3

  View details for Web of Science ID 000403753100001

• Beyond prediction: Using big data for policy problems. Science (New York, N.Y.) Athey, S. n. 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

• 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

• 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

• Machine Learning and Causal Inference for Policy Evaluation Athey, S., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2015: 5-6

  View details for DOI 10.1145/2783258.2785466

  View details for Web of Science ID 000485312900003

• 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