Research

My papers are organized by topic to make them easier to find. There is some duplication/cross-listing. For a chronological list of my articles look at my cv. 

Conti, Rena, Richard Frank, and Jonathan Gruber. “Generic Drug Repurposing for COVID-19 and Beyond.” Boston University, Institute for Health Innovation Policy (2020).

https://www.bu.edu/ihsip/2020/07/17/generic-drug-repurposing-for-covid-19-and-beyond/

Topic(s): Health

Athey, Susan, Kosuke Inoue, and Yusuke Tsugawa. “Targeted Treatment Assignment Using Data from Randomized Experiments with Noncompliance.” Available at SSRN 5214522.

https://ssrn.com/abstract=5214522

https://dx.doi.org/10.2139/ssrn.5214522

Topic(s): Econometric Theory and Machine Learning

Clarke, Damian, Daniel Pailañir, Susan Athey, and Guido Imbens “On Synthetic Difference-in-Differences and Related Estimation Methods in Stata.” The Stata Journal 24, no. 4 (2024): 557-598.

https://doi.org/10.1177/1536867X24129791

Topic(s): Econometric Theory and Machine Learning

Athey, Susan. “Presidential Address: The Economist as Designer in the Innovation Process for Socially Impactful Digital Products.” American Economic Review 115, no. 4 (2025): 1059-1099.

https://pubs.aeaweb.org/doi/pdf/10.1257/aer.115.4.1059

Topic(s): Econometric Theory and Machine Learning; Social Impact

Athey, Susan, Niall Keleher, and Jann Spiess. “Machine learning who to nudge: causal vs predictive targeting in a field experiment on student financial aid renewal.” Journal of Econometrics (2025): 105945.

https://doi.org/10.1016/j.jeconom.2024.105945

Topic(s): Econometric Theory and Machine Learning

Vafa, Keyon, Susan Athey, and David M. Blei. “Estimating Wage Disparities Using Foundation Models.” Proceedings of the National Academy of Sciences 122, no. 22 (2025): e2427298122.

https://doi.org/10.1073/pnas.2427298122

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions

Athey, Susan, Raj Chetty, Guido W. Imbens, and Hyunseung Kang. “The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely.” Review of Economic Studies, 2025.

https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdaf087/8268796

Topic(s): Econometric Theory and Machine Learning

Vafa, Keyon, Susan Athey, and David M. Blei. “Decomposing Changes in the Gender Wage Gap over Worker Careers.” (2023).

https://conference.nber.org/conf_papers/f189605.pdf

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions

Du, Tianyu, Ayush Kanodia, Herman Brunborg, Keyon Vafa, and Susan Athey. “LABOR-LLM: Language-Based Occupational Representations with Large Language Models.”arXiv preprint arXiv:2406.17972 (2024).

https://doi.org/10.48550/arXiv.2406.17972

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions

Krishnamurthy, Sanath Kumar, Susan Athey, and Emma Brunskill. “Data-Driven Error Estimation: Upper Bounding Multiple Errors with No Technical Debt.” arXiv preprint, 2024.

https://arxiv.org/abs/2405.04636

Topic(s): Econometric Theory and Machine Learning

Vafa, Keyon, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, and David Blei. “CAREER: A Foundation Model for Labor Sequence Data.” Transactions on Machine Learning Research (2024)

https://openreview.net/forum?id=4i1MXH8Sle

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions

Athey, Susan, Lisa K. Simon, Oskar N. Skans, Johan Vikström, and Yaroslav Yakymovyc. “The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets.”arXiv preprint arXiv:2307.06684 (2023).

https://doi.org/10.48550/arXiv.2307.06684

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions; Social Impact

Carranza, Aldo Gael, and Susan Athey. “Robust Offline Policy Learning with Observational Data from Multiple Sources.” arXiv preprint arXiv:2410.08537 (2024)

Topic(s): Econometric Theory and Machine Learning

Xiong, Ruoxuan, Susan Athey, Mohsen Bayati, and Guido Imbens. “Optimal Experimental Design for Staggered Rollouts.” Management Science (2023).

https://doi.org/10.1287/mnsc.2023.4928.

Topic(s): Econometric Theory and Machine Learning

Athey, Susan, Peter J. Bickel, Aiyou Chen, Guido W. Imbens, and Michael Pollmann. “Semi-parametric estimation of treatment effects in randomised experiments.” Journal of the Royal Statistical Society Series B: Statistical Methodology 85, no. 5 (2023): 1615-1638.

https://doi-org.stanford.idm.oclc.org/10.1093/jrsssb/qkad072

Topic(s): Econometric Theory and Machine Learning

Krishnamurthy, Sanath Kumar, and Susan Athey. “Optimal Model Selection in Contextual Bandits with Many Classes via Offline Oracles.” arXiv preprint No. 3971 (2021).

https://doi.org/10.48550/arXiv.2106.06483

Topic(s): Econometric Theory and Machine Learning

Sverdrup, Erik, Han Wu, Susan Athey, and Stefan Wager. “Qini curves for multi-armed treatment rules.” Journal of Computational and Graphical Statistics (2024): 1-24

https://doi.org/10.1080/10618600.2024.2418820

Topic(s): Econometric Theory and Machine Learning

Krishnamurthy, Sanath Kumar, Ruohan Zhan, Susan Athey, and Emma Brunskill. “Proportional response: Contextual bandits for simple and cumulative regret minimization.” Advances in Neural Information Processing Systems 36 (2023): 30255-30266.

https://arxiv.org/abs/2307.02108

Topic(s): Econometric Theory and Machine Learning

Carranza, Aldo Gael, and Susan Athey. “Federated Offline Policy Learning with Heterogeneous Observational Data.” arXiv preprint arXiv:2305.12407 (2023).

https://doi.org/10.48550/arXiv.2305.12407

Topic(s): Econometric Theory and Machine Learning

Du, Tianyu, Ayush Kanodia, and Susan Athey. “Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python.” arXiv preprint (2023).

https://arxiv.org/abs/2304.01906

Topic(s): Econometric Theory and Machine Learning

Carranza, Aldo Gael, Sanath Kumar Krishnamurthy, and Susan Athey. “Flexible and efficient contextual bandits with heterogeneous treatment effect oracles.” In International Conference on Artificial Intelligence and Statistics, pp. 7190-7212. PMLR, 2023.

https://proceedings.mlr.press/v206/carranza23a/carranza23a.pdf

Topic(s): Econometric Theory and Machine Learning

Athey, Susan, Undral Byambadalai, Vitor Hadad, Sanath Kumar Krishnamurthy, Weiwen Leung, and Joseph Jay Williams. “Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning.” arXiv preprint (2022).

https://doi.org/10.48550/arXiv.2211.12004

Topic(s): Econometric Theory and Machine Learning; Social Impact

Vafa, Keyon, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, and David M. Blei. “CAREER: Transfer Learning for Economic Prediction of Labor Sequence Data.” Working paper (2022).

https://www.gsb.stanford.edu/gsb-box/route-download/620742

Topic(s): Econometric Theory and Machine Learning

Zeng, Jiaming, Michael F. Gensheimer, Daniel L. Rubin, Susan Athey, and Ross D. Shachter. “Uncovering Interpretable Potential Confounders in Electronic Medical Records.” Nature Communications 13 (2022).

https://doi.org/10.1038/s41467-022-28546-8

Topic(s): Econometric Theory and Machine Learning; Health

Cui, Peng, and Susan Athey. “Stable Learning Establishes Some Common Ground between Causal Inference and Machine Learning.” Nature Machine Intelligence 4 (2) (2022): 110–115.

https://doi.org/10.1038/s42256-022-00445-z

Topic(s): Econometric Theory and Machine Learning

Arkhangelsky, Dmitry, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager. “Synthetic Difference-in-Differences.” American Economic Review 111 (12) (2021): 4088–4118.

https://doi.org/10.1257/aer.20190159

Topic(s): Econometric Theory and Machine Learning

Donnelly, Robert, Francisco J. R. Ruiz, David Blei, et al. “Counterfactual Inference for Consumer Choice Across Many Product Categories.” Quantitative Marketing and Economics 19 (2021): 369–407.

https://doi.org/10.1007/s11129-021-09241-2.

Topic(s): Econometric Theory and Machine Learning; Industrial Organization and the Econometrics of IO

Athey, Susan, Peter J. Bickel, Aiyou Chen, Guido Imbens, and Michael Pollmann. “Semiparametric Estimation of Treatment Effects in Randomized Experiments.” National Bureau of Economic Research Working Paper No. w29242 (2021).

https://www.nber.org/papers/w29242

Topic(s): Econometric Theory and Machine Learning

Xiong, Ruoxuan, Allison Koenecke, Michael Powell, Zhu Shen, and Joshua T. Vogelstein. “Federated Causal Inference in Heterogeneous Observational Data.” arXiv preprint (2021).

https://arxiv.org/abs/2107.11732

Topic(s): Econometric Theory and Machine Learning

Imbens, Guido. “Breiman’s Two Cultures: A Perspective from Econometrics.” Observational Studies 7 (1) (2021).

https://muse.jhu.edu/article/799753

Topic(s): Econometric Theory and Machine Learning

Krishnamurthy, Sanath Kumar, and Susan Athey. “Optimal Model Selection in Contextual Bandits with Many Classes via Offline Oracles.” arXiv preprint No. 3971 (2021).

https://arxiv.org/abs/2106.06483v1

Topic(s): Econometric Theory and Machine Learning

Zhan, Ruohan, Vitor Hadad, and David A. Hirshberg. “Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits.” arXiv preprint (2021).

https://arxiv.org/abs/2106.02029

Topic(s): Econometric Theory and Machine Learning

Hofman, Jake M., and Duncan J. Watts. “Integrating Explanation and Prediction in Computational Social Science.” Nature 594 (2021).

https://doi.org/10.1038/s41586-021-03659-0

Topic(s): Econometric Theory and Machine Learning

Zhan, Ruohan, Zhimei Ren, and Zhengyuan Zhou. “Policy Learning with Adaptively Collected Data.” arXiv preprint (2021).

https://arxiv.org/abs/2105.02344

Topic(s): Econometric Theory and Machine Learning

Athey, Susan, and Guido W. Imbens. “Design-Based Analysis in Difference-in-Differences Settings with Staggered Adoption.” Journal of Econometrics 226 (1) (2022): 62–79.

https://doi.org/10.1016/j.jeconom.2020.10.012

Topic(s): Econometric Theory and Machine Learning

Athey, Susan, Guido W. Imbens, Jonas Metzger, and Evan Munro. “Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations.” Journal of Econometrics 240 (2) (2024): 105076.

https://doi.org/10.1016/j.jeconom.2020.09.013

Topic(s): Econometric Theory and Machine Learning

Krishnamurthy, Sanath Kumar, and Vitor Hadad. “Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles.” arXiv preprint (2021).

https://arxiv.org/abs/2102.13240

Topic(s): Econometric Theory and Machine Learning

Hadad, Vitor, David A. Hirshberg, Ruohan Zhan, Stefan Wager, and Susan Athey. “Confidence Intervals for Policy Evaluation in Adaptive Experiments.” Proceedings of the National Academy of Sciences 118 (15) (2021).

https://arxiv.org/abs/1911.02768

Topic(s): Econometric Theory and Machine Learning

Athey, Susan, and Stefan Wager. “Policy Learning with Observational Data.” Econometrica 89 (1) (2021): 133–161.

https://doi.org/10.3982/ECTA15732

Topic(s): Econometric Theory and Machine Learning

Krishnamurthy, Sanath Kumar, Vitor Hadad, and Susan Athey. “Tractable Contextual Bandits Beyond Realizability.” In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research 130: 1423–1431 (2021). PMLR.

https://proceedings.mlr.press/v130/kumar-krishnamurthy21a.html

Topic(s): Econometric Theory and Machine Learning

Friedberg, Rina, Julie Tibshirani, and Stefan Wager. “Local Linear Forests.” Journal of Computational and Graphical Statistics (2020).

https://arxiv.org/abs/1807.11408

Topic(s): Econometric Theory and Machine Learning

Chetty, Raj, and Guido Imbens. “Combining Experimental and Observational Data to Estimate Treatment Effects on Long-Term Outcomes.” arXiv preprint (2020).

https://arxiv.org/abs/2006.09676

Topic(s): Econometric Theory and Machine Learning

Sverdrup, Erik, Ayush Kanodia, Zhengyuan Zhou, and Stefan Wager. “Policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees.” Journal of Open Source Software 5 (50) (2020): 2232.

https://joss.theoj.org/papers/10.21105/joss.02232.pdf

Topic(s): Econometric Theory and Machine Learning

Kuang, Kun, Ruoxuan Xiong, Peng Cui, Susan Athey, and Bo Li. “Stable prediction with model misspecification and agnostic distribution shift.” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 4485-4492. 2020.

https://doi.org/10.1609/aaai.v34i04.5876

Topic(s): Econometric Theory and Machine Learning

Athey, Susan, Francisco J. R. Ruiz, and David M. Blei. “SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements.” Annals of Applied Statistics 14, no. 1 (March 2020): 1–27.

https://www.gsb.stanford.edu/faculty-research/publications/shopper-probabilistic-model-consumer-choice-substitutes-complements

Topic(s): Econometric Theory and Machine Learning; Industrial Organization and the Econometrics of IO

Krishnamurthy, Sanath Kumar. “Survey Bandits with Regret Guarantees.” arXiv preprint (2020).

https://arxiv.org/abs/2002.09814

Topic(s): Econometric Theory and Machine Learning

Xiong, Ruoxuan, Mohsen Bayati, and Guido Imbens. “Optimal Experimental Design for Staggered Rollouts.” arXiv preprint (2019).

https://arxiv.org/abs/1911.03764

Topic(s): Econometric Theory and Machine Learning

Johannemann, Jonathan, Vitor Hadad, and Stefan Wager. “Sufficient Representations for Categorical Variables.” arXiv preprint (2019).

https://arxiv.org/abs/1908.09874

Topic(s): Econometric Theory and Machine Learning

Wager, Stefan. “Estimating Treatment Effects with Causal Forests: An Application.” Observational Studies 5 (2019): 21–35.

https://obsstudies.org/277-2/

Topic(s): Econometric Theory and Machine Learning

Arkhangelsky, Dmitry, David A. Hirshberg, Guido W. Imbens, and Stefan Wager. “Synthetic Difference in Differences.” arXiv preprint (2019).

https://arxiv.org/pdf/1812.09970.pdf.

Topic(s): Econometric Theory and Machine Learning

Bayati, Mohsen, Guido Imbens, and Zhaonan Qu. “Ensemble Methods for Causal Effects in Panel Data Settings.” American Economic Review Papers and Proceedings 109 (2019): 65–70.

https://doi.org/10.1257/pandp.20191069

Topic(s): Econometric Theory and Machine Learning

Imbens, Guido. “Machine Learning Methods Economists Should Know About.” arXiv preprint (2019)

https://arxiv.org/abs/1903.10075.

Topic(s): Econometric Theory and Machine Learning

Tibshirani, Julie, and Stefan Wager. “Generalized Random Forests.” Annals of Statistics 47 (2) (2019): 1148–1178.

https://doi.org/10.1214/18-AOS1709

Topic(s): Econometric Theory and Machine Learning

Zhou, Zhengyuan, Panayotis Mertikopoulos, Nicholas Bambos, Peter Glynn, and Yinyu Ye. “Learning in Games with Lossy Feedback.” In Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada.

http://papers.nips.cc/paper/7760-learning-in-games-with-lossy-feedback.pdf

Topic(s): Econometric Theory and Machine Learning

Zhou, Zhengyuan, and Stefan Wager. “Offline Multi-Action Policy Learning: Generalization and Optimization.” arXiv preprint (2018).

https://arxiv.org/abs/1810.04778

Topic(s): Econometric Theory and Machine Learning

Kuang, Kun, Ruoxuan Xiong, Peng Cui, and Bo Li. “Stable Predictions across Unknown Environments.” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018). arXiv preprint (2018).

https://arxiv.org/abs/1806.06270

Topic(s): Econometric Theory and Machine Learning

Wager, Stefan. “Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests.” Journal of the American Statistical Association (2018): 1–15.

https://doi.org/10.1080/01621459.2017.1319839.

Topic(s): Econometric Theory and Machine Learning

Athey, Susan, David Blei, Robert Donnelly, Francisco Ruiz, and Tobias Schmidt. “Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data.” AEA Papers and Proceedings 108 (2018): 64–67.

https://doi.org/10.1257/pandp.20181031

Topic(s): Econometric Theory and Machine Learning; Industrial Organization and the Econometrics of IO

Imbens, Guido W., and Stefan Wager. “Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions.” Journal of the Royal Statistical Society: Series B 80 (4) (2018): 597–623.

https://doi.org/10.1111/rssb.12268

Topic(s): Econometric Theory and Machine Learning

Dimakopoulou, Maria, and Guido Imbens. “Estimation Considerations in Contextual Bandits.” arXiv preprint (2018).

https://arxiv.org/abs/1711.07077

Topic(s): Econometric Theory and Machine Learning

Athey, Susan. “The Impact of Machine Learning on Economics.” Forthcoming in The Economics of Artificial Intelligence: An Agenda.

http://www.nber.org/chapters/c14009.pdf.

Topic(s): Econometric Theory and Machine Learning

Liu, Liping, Francisco Ruiz, and David Blei. “Context Selection for Embedding Models.” In Proceedings of the Neural Information Processing Systems (NIPS 2017), 4819–4828. 2017.

http://papers.nips.cc/paper/7067-context-selection-for-embedding-models

Topic(s): Econometric Theory and Machine Learning

Rudolph, Maja, Francisco Ruiz, and David Blei. “Structured Embedding Models for Grouped Data.” In Proceedings of the Neural Information Processing Systems (NIPS 2017), 250–260. 2017.

https://papers.nips.cc/paper/6629-structured-embedding-models-for-grouped-data.

Topic(s): Econometric Theory and Machine Learning

Abadie, Alberto, Guido Imbens, and Jeffrey Wooldridge. “When Should You Adjust Standard Errors for Clustering?” arXiv preprint (2017).

https://arxiv.org/abs/1710.02926.

Topic(s): Econometric Theory and Machine Learning

Bayati, Mohsen, Nikolay Doudchenko, Guido Imbens, and Khashayar Khosravi. (2017). “Matrix Completion Methods for Causal Panel Data Models.” arXiv preprint.

https://arxiv.org/abs/1710.10251.

Topic(s): Econometric Theory and Machine Learning

Abadie, Alberto, Guido W. Imbens, and Jeffrey M. Wooldridge. “Sampling-Based vs. Design-Based Uncertainty in Regression Analysis (Previously Titled: Finite Population Causal Standard Errors).” Econometrica (forthcoming). arXiv preprint (2017).

https://arxiv.org/abs/1706.01778v1.

Topic(s): Econometric Theory and Machine Learning

Imbens, Guido. “The State of Applied Econometrics: Causality and Policy Evaluation.” Journal of Economic Perspectives 31 (2) (2017): 3–32.

https://doi.org/10.1257/jep.31.2.3

Topic(s): Econometric Theory and Machine Learning

Imbens, Guido, Thai Pham, and Stefan Wager. “Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges.” American Economic Review 107 (5) (2017): 278–281.

https://doi.org/10.1257/aer.p20171042

Topic(s): Econometric Theory and Machine Learning

Imbens, Guido. “The Econometrics of Randomized Experiments.” In Handbook of Economic Field Experiments, 1 (2017): 73–140.

https://www.elsevier.com/books/handbook-of-field-experiments/duflo/978-0-444-63324-8.

Topic(s): Econometric Theory and Machine Learning

Eckles, Dean, and Guido W. Imbens. “Exact P-Values for Network Interference.” Journal of the American Statistical Association (2017).

https://doi.org/10.1080/01621459.2016.1241178.

Topic(s): Econometric Theory and Machine Learning

Athey, Susan. “Beyond Prediction: Using Big Data for Policy Problems.” Science 355 (2017): 483–485.

https://doi.org/10.1126/science.aal4321

Topic(s): Econometric Theory and Machine Learning; Industrial Organization and the Econometrics of IO

Chetty, Raj, Guido W. Imbens, and Hyunseung Kang. “Estimating Treatment Effects Using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index.” arXiv preprint (2016).

http://arxiv.org/abs/1603.09326

Topic(s): Econometric Theory and Machine Learning

Imbens, Guido W. “Recursive Partitioning for Heterogeneous Causal Effects.” Proceedings of the National Academy of Sciences 113 (27) (2016): 7353–7360.

https://doi.org/10.1073/pnas.1510489113

Topic(s): Econometric Theory and Machine Learning

Athey, Susan. “Machine Learning and Causal Inference for Policy Evaluation.” In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’15), 5–6. Association for Computing Machinery, New York, NY, USA, (2015).

https://doi.org/10.1145/2783258.2785466

Topic(s): Econometric Theory and Machine Learning

Imbens, Guido W. “A Measure of Robustness to Misspecification.” The American Economic Review 105 (5) (2015): 476–480.

https://doi.org/10.1257/aer.p20151020

Topic(s): Econometric Theory and Machine Learning

Kirgios, Erika, Susan Athey, Angela L. Duckworth, Dean Karlan, Michael Luca, Katherine L. Milkman, and Molly Offer-Westort. Does Q&A Boost Engagement? Health Messaging Experiments in the US and Ghana. No. w33294. National Bureau of Economic Research, 2025.

https://www.nber.org/papers/w33294

Topic(s): Health; Social Impact

Athey, Susan. “Presidential Address: The Economist as Designer in the Innovation Process for Socially Impactful Digital Products.” American Economic Review 115, no. 4 (2025): 1059-1099.

https://pubs.aeaweb.org/doi/pdf/10.1257/aer.115.4.1059

Topic(s): Econometric Theory and Machine Learning; Social Impact

Athey, Susan. “Choosing the ‘Right’ Default Donation Amounts for Each Donor to Balance Multiple Fundraising Objectives.” Golub Capital Social Impact Lab. Stanford Graduate School of Business, November 2024.

https://www.gsb.stanford.edu/faculty-research/publications/choosing-right-default-donation-amounts-each-donor-balance-multiple.

Topic(s): Social Impact

Athey, Susan, Niall Keleher, and Jann Spiess. “Machine learning who to nudge: causal vs predictive targeting in a field experiment on student financial aid renewal.” arXiv preprint arXiv:2310.08672 (2023).

https://doi.org/10.48550/arXiv.2310.08672

Topic(s): Social Impact

Inoue, Kosuke, Susan Athey, Katherine Baicker, and Yusuke Tsugawa. “Heterogeneous effects of Medicaid coverage on cardiovascular risk factors: secondary analysis of randomized controlled trial.” bmj 386 (2024).

https://doi.org/10.1136/bmj-2024-079377

Topic(s): Social Impact

Athey, Susan, Undral Byambadalai, Matias Cersosimo, Kristine Koutout, and Shanjukta Nath. “The Heterogeneous Impact of Changes in Default Gift Amounts on Fundraising.” SSRN preprint (2024).

https://doi.org/10.2139/ssrn.4785704.

Topic(s): Social Impact

Athey, Susan, and Emil Palikot. “The Value of Non-Traditional Credentials in the Labor Market.”arXiv preprint arXiv:2405.00247 (2024).

https://doi.org/10.48550/arXiv.2405.00247

Topic(s): Labor Market Transitions; Social Impact

Offer-Westort, Molly, Leah R. Rosenzweig, and Susan Athey. “Battling the coronavirus ‘infodemic’ among social media users in Kenya and Nigeria.” Nature Human Behaviour 8, no. 5 (2024): 823-834.

https://doi.org/10.1038/s41562-023-01810-7

Topic(s): Social Impact

Athey, Susan, Shawn Allen Cole, Shanjukta Nath, and S. Jessica Zhu. “Targeting, Personalization, and Engagement in an Agricultural Advisory Service.” SSRN preprint (2023).

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4536641.

Topic(s): Social Impact

Athey, Susan, Lisa K. Simon, Oskar N. Skans, Johan Vikström, and Yaroslav Yakymovyc. “The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets.”arXiv preprint arXiv:2307.06684 (2023).

https://doi.org/10.48550/arXiv.2307.06684

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions; Social Impact

Athey, Susan, Matias Cersosimo, Dean Karlan, Kristine Koutout, and Henrike Steimer. “Impact Matters for Giving at Checkout.” SSRN preprint (2024).

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4711399.

Topic(s): Social Impact

Agrawal, Keshav, Susan Athey, Ayush Kanodia, and Emil Palikot. “Digital interventions and habit formation in educational technology.” arXiv preprint arXiv:2310.10850 (2023).

https://doi.org/10.48550/arXiv.2310.10850

Topic(s): Social Impact

Athey, Susan, Katy Bergstrom, Vitor Hadad, Julian C. Jamison, Berk Özler, Luca Parisotto, and Julius Dohbit Sama. “Can personalized digital counseling improve consumer search for modern contraceptive methods?” Science Advances 9, no. 40 (2023): eadg4420.

https://doi.org/10.1126/sciadv.adg4420

Topic(s): Social Impact

Athey, Susan, Matias Cersosimo, Kristine Koutout, and Zelin Li. “Emotion-versus reasoning-based drivers of misinformation sharing: A field experiment using text message courses in Kenya.” (2023).

https://dx.doi.org/10.2139/ssrn.4489759

Topic(s): Social Impact

Inoue, Kosuke, Susan Athey, and Yusuke Tsugawa. “Machine-Learning-Based High-Benefit Approach versus Conventional High-Risk Approach in Blood Pressure Management.” International Journal of Epidemiology (2023).

https://doi.org/10.1093/ije/dyad037

Topic(s): Social Impact

Athey, Susan, Dean Karlan, Emil Palikot, and Yuan Yuan. “Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces.” National Bureau of Economic Research Working Paper No. w30633 (2022).

https://www.nber.org/papers/w30633

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Social Impact

Offer-Westort, Molly, Leah R. Rosenzweig, and Susan Athey. “Battling the Coronavirus Infodemic Among Social Media Users in Africa.” Working paper (2023).

https://www.gsb.stanford.edu/gsb-box/route-download/620740.

Topic(s): Social Impact

Athey, Susan, Kristen Grabarz, Michael Luca, and Nils Wernerfelt. “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 120 (5) (2023).

https://doi.org/10.1073/pnas.220811012

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Health; Social Impact

Agrawal, Keshav, Susan Athey, Ayush Kanodia, and Emil Palikot. “Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial.” arXiv preprint (2022).

https://arxiv.org/pdf/2208.13940

Topic(s): Social Impact

Athey, Susan, and Emil Palikot. “Effective and Scalable Programs to Facilitate Labor Market Transitions for Women in Technology.” arXiv preprint arXiv:2211.09968 (2022).

https://doi.org/10.48550/arXiv.2211.09968

Topic(s): Labor Market Transitions; Social Impact

Athey, Susan, Undral Byambadalai, Vitor Hadad, Sanath Kumar Krishnamurthy, Weiwen Leung, and Joseph Jay Williams. “Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning.” arXiv preprint (2022).

https://doi.org/10.48550/arXiv.2211.12004

Topic(s): Econometric Theory and Machine Learning; Social Impact

Bergstrom, Katy, Vitor Hadad, Julian C. Jamison, Berk Özler, Luca Parisotto, and Julius Dohbit Sama. “Shared Decision-Making: Can Improved Counseling Increase Willingness to Pay for Modern Contraceptives?” Working Paper. World Bank (2021).

http://hdl.handle.net/10986/36304

Topic(s): Social Impact

Athey, Susan, Billy Ferguson, Matthew Gentzkow, and Tobias Schmidt. “Estimating Experienced Racial Segregation in US Cities Using Large-Scale GPS Data.” Proceedings of the National Academy of Sciences 118 (46) (2021): e2026160118.

https://doi.org/10.1073/pnas.2026160118.

Topic(s): Social Impact

Castillo, Juan Camilo, Amrita Ahuja, Susan Athey, Arthur Baker, Eric Budish, Tasneem Chipty, Rachel Glennerster, et al. “Market Design to Accelerate COVID-19 Vaccine Supply.” Science 371, no. 6534 (2021): 1107–9

https://www.science.org/doi/full/10.1126/science.abg0889

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Health; Social Impact

Egger, Dennis, Edward Miguel, Shana S. Warren, Ashish Shenoy, Elliott Collins, Dean Karlan, Doug Parkerson, et al. “Falling Living Standards during the COVID-19 Crisis: Quantitative Evidence from Nine Developing Countries.” Science Advances 7 (6) (2021): eabe0997.

https://doi.org/10.1126/sciadv.abe0997.

Topic(s): Social Impact

Athey, Susan, Dean Karlan, Emil Palikot, and Yuan Yuan. “Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces.” National Bureau of Economic Research Working Paper No. w30633 (2022).

https://www.nber.org/papers/w30633

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Social Impact

Athey, Susan, Kristen Grabarz, Michael Luca, and Nils Wernerfelt. “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 120 (5) (2023).

https://doi.org/10.1073/pnas.220811012

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Health; Social Impact

Athey, Susan, Juan Camilo Castillo, Esha Chaudhuri, Michael Kremer, Alexandre Simoes Gomes, and Christopher M. Snyder. “Expanding Capacity for Vaccines against COVID-19 and Future Pandemics: A Review of Economic Issues.” Oxford Review of Economic Policy 38 (4) (2022): 742–770.

https://www.nber.org/papers/w30192

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Health

Castillo, Juan Camilo, Amrita Ahuja, Susan Athey, Arthur Baker, Eric Budish, Tasneem Chipty, Rachel Glennerster, et al. “Market Design to Accelerate COVID-19 Vaccine Supply.” Science 371, no. 6534 (2021): 1107–9

https://www.science.org/doi/full/10.1126/science.abg0889

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Health; Social Impact

Babaioff, Moshe, Michael D. Grubb, and Ittai Abraham. “Peaches, Lemons, and Cookies: Designing Auction Markets with Dispersed Information.” Games and Economic Behavior 124 (2020): 454–477.

https://doi.org/10.1016/j.geb.2020.09.004

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics)

Athey, Susan, Emilio Calvano, and Joshua S. Gans. “The Impact of Consumer Multi-Homing on Advertising Markets and Media Competition.” Rotman School of Management Working Paper No. 2180851 (2016).

https://doi.org/10.2139/ssrn.2180851.

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics)

Catalini, Christian, and Catherine E. Tucker. “The Digital Privacy Paradox: Small Money, Small Costs, Small Talk.” Working Paper (2017).

https://www.nber.org/papers/w23488.

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics)

Athey, Susan. “Information, Privacy and the Internet: An Economic Perspective.” CPB Netherlands Bureau for Economic Policy Analysis (2014).

http://www.cpb.nl/sites/default/files/CPB-Lecture-2014-Information-Privacy-and-the-Internet-an-economic-perspective.pdf.

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics)

Coey, Dominic, and Jonathan Levin. “Set-Asides and Subsidies in Auctions.” American Economic Journal: Microeconomics 5 (1) (2013): 1–27.

https://doi.org/10.1257/mic.5.1.1

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Industrial Organization and the Econometrics of IO

Mobius, Markus, and Jeno Pal. “The Impact of Aggregators on Internet News Consumption.” Working Paper (2017).

https://www.nber.org/papers/w28746.

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Industrial Organization and the Econometrics of IO

Athey, Susan, Juan Camilo Castillo, and Bharat Chandar. “Service Quality on Online Platforms: Empirical Evidence about Driving Quality at Uber.” Forthcoming in Management Science.

https://doi.org/10.3386/w33087.

Topic(s): Industrial Organization and the Econometrics of IO

Athey, Susan, Mark Chicu, Malika Krishna, and Ioana Marinescu. “The Year in Review: Economics at the Antitrust Division, 2022–2023.” Review of Industrial Organization 63 (2024): 525–544.

https://link.springer.com/article/10.1007/s11151-023-09931-z.

Topic(s): Industrial Organization and the Econometrics of IO

Athey, Susan, and Fiona Scott Morton. “Platform Annexation.” Antitrust Law Journal 84 (3) (2022): 677–703.

https://www.gsb.stanford.edu/gsb-box/route-download/620540.

Topic(s): Industrial Organization and the Econometrics of IO

Athey, Susan, Russell Pittman, and Fan Zhang. “The Year in Review: Economics at the Antitrust Division 2021–2022.” Review of Industrial Organization 61 (2022): 439–447.

https://doi.org/10.1007/s11151-022-09884-9.

Topic(s): Industrial Organization and the Econometrics of IO

Donnelly, Robert, Francisco J. R. Ruiz, David Blei, et al. “Counterfactual Inference for Consumer Choice Across Many Product Categories.” Quantitative Marketing and Economics 19 (2021): 369–407.

https://doi.org/10.1007/s11129-021-09241-2.

Topic(s): Econometric Theory and Machine Learning; Industrial Organization and the Econometrics of IO

Vogelstein, Joshua T., Michael Powell, Allison Koenecke, Ruoxuan Xiong, Nicole Fischer, et al. “Alpha-1 Adrenergic Receptor Antagonists for Preventing Acute Respiratory Distress Syndrome and Death from Cytokine Storm Syndrome.” arXiv preprint (2020).

https://arxiv.org/abs/2004.10117.

Topic(s): Industrial Organization and the Econometrics of IO

Athey, Susan, Francisco J. R. Ruiz, and David M. Blei. “SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements.” Annals of Applied Statistics 14, no. 1 (March 2020): 1–27.

https://www.gsb.stanford.edu/faculty-research/publications/shopper-probabilistic-model-consumer-choice-substitutes-complements

Topic(s): Econometric Theory and Machine Learning; Industrial Organization and the Econometrics of IO

Ferguson, Billy, Matthew Gentzkow, and Tobias Schmidt. “Experienced Segregation.” Working Paper (2019).

http://web.stanford.edu/~gentzkow/research/experienced-segregation.pdf

Topic(s): Industrial Organization and the Econometrics of IO

Athey, Susan, David Blei, Robert Donnelly, Francisco Ruiz, and Tobias Schmidt. “Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data.” AEA Papers and Proceedings 108 (2018): 64–67.

https://doi.org/10.1257/pandp.20181031

Topic(s): Econometric Theory and Machine Learning; Industrial Organization and the Econometrics of IO

Athey, Susan. “Beyond Prediction: Using Big Data for Policy Problems.” Science 355 (2017): 483–485.

https://doi.org/10.1126/science.aal4321

Topic(s): Econometric Theory and Machine Learning; Industrial Organization and the Econometrics of IO

Parashkevov, Iva, Vishnu Sarukkai, and Jing Xia. “Bitcoin Pricing, Adoption, and Usage: Theory and Evidence.” Working Paper (2016).

http://www.gsb.stanford.edu/faculty-research/working-papers/bitcoin-pricing-adoption-usage-theory-evidence.

Topic(s): Industrial Organization and the Econometrics of IO

Coey, Dominic, and Jonathan Levin. “Set-Asides and Subsidies in Auctions.” American Economic Journal: Microeconomics 5 (1) (2013): 1–27.

https://doi.org/10.1257/mic.5.1.1

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Industrial Organization and the Econometrics of IO

Mobius, Markus, and Jeno Pal. “The Impact of Aggregators on Internet News Consumption.” Working Paper (2017).

https://www.nber.org/papers/w28746.

Topic(s): Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics); Industrial Organization and the Econometrics of IO

Calvano, Emilio, and Saumitra Jha. “A Theory of Community Formation and Social Hierarchy.” Working Paper (2016).

https://doi.org/10.2139/ssrn.2823777.

Topic(s): Dynamic and Repeated Games and Mechanisms; Economics of Organizations

Ellison, Glenn. “Dynamics of Open Source Movements.” Journal of Economics & Management Strategy 23 (2) (2014): 294–316.

https://doi.org/10.1111/jems.12053.

Topic(s): Dynamic and Repeated Games and Mechanisms; Economics of Organizations

Segal, Ilya. “An Efficient Dynamic Mechanism.” Econometrica 81 (6) (2013): 2463–2485.

https://doi.org/10.3982/ECTA6995.

Topic(s): Dynamic and Repeated Games and Mechanisms

Gans, Joshua, and Kevin A. Bryan. “The Allocation of Decision Authority to Human and Artificial Intelligence.” NBER Working Paper no. 26673 (2020).

https://doi.org/10.3386/w26673

Topic(s): Economics of Organizations

Luca, Michael. “Economists (and Economics) in Tech Companies.” Journal of Economic Perspectives 33 (1) (2019): 209–230.

https://www.aeaweb.org/articles?id=10.1257/jep.33.1.209.

Topic(s): Economics of Organizations

Calvano, Emilio, and Saumitra Jha. “A Theory of Community Formation and Social Hierarchy.” Working Paper (2016).

https://doi.org/10.2139/ssrn.2823777.

Topic(s): Dynamic and Repeated Games and Mechanisms; Economics of Organizations

Stern, Scott. “The Nature and Incidence of Software Piracy: Evidence from Windows.” In Economic Analysis of the Digital Economy, edited by Avi Goldfarb, Shane Greenstein, and Catherine Tucker, 443–477. Chicago: University of Chicago Press, 2015.

http://www.nber.org/chapters/c13002.pdf

Topic(s): Economics of Organizations

Ellison, Glenn. “Dynamics of Open Source Movements.” Journal of Economics & Management Strategy 23 (2) (2014): 294–316.

https://doi.org/10.1111/jems.12053.

Topic(s): Dynamic and Repeated Games and Mechanisms; Economics of Organizations

Levin, Jonathan. “The Value of Information in Monotone Decision Problems.” Research in Economics (2017).

https://doi.org/10.1016/j.rie.2017.01.001.

Topic(s): Monotone Comparative Statics and Applications

Athey, Susan, Alex Gross, Ioana Marinescu, and Jennifer Shanefelter. “The Year in Review: Economics at the Antitrust Division 2023–2024.” Review of Industrial Organization (2024): 1-21.

https://link.springer.com/article/10.1007/s11151-024-09998-2

Topic(s): Empirical Industrial Organization

Vafa, Keyon, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, and David M. Blei. “CAREER: Economic prediction of labor sequence data under distribution shift.” In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications. 2022.

https://doi.org/10.48550/arXiv.2202.08370

Topic(s): Econometric Theory and Machine Learning

Vafa, Keyon, Susan Athey, and David M. Blei. “Estimating Wage Disparities Using Foundation Models.” Proceedings of the National Academy of Sciences 122, no. 22 (2025): e2427298122.

https://doi.org/10.1073/pnas.2427298122

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions

Vafa, Keyon, Susan Athey, and David M. Blei. “Decomposing Changes in the Gender Wage Gap over Worker Careers.” (2023).

https://conference.nber.org/conf_papers/f189605.pdf

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions

Athey, Susan, and Emil Palikot. “The Value of Non-Traditional Credentials in the Labor Market.”arXiv preprint arXiv:2405.00247 (2024).

https://doi.org/10.48550/arXiv.2405.00247

Topic(s): Labor Market Transitions; Social Impact

Du, Tianyu, Ayush Kanodia, Herman Brunborg, Keyon Vafa, and Susan Athey. “LABOR-LLM: Language-Based Occupational Representations with Large Language Models.”arXiv preprint arXiv:2406.17972 (2024).

https://doi.org/10.48550/arXiv.2406.17972

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions

Vafa, Keyon, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, and David Blei. “CAREER: A Foundation Model for Labor Sequence Data.” Transactions on Machine Learning Research (2024)

https://openreview.net/forum?id=4i1MXH8Sle

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions

Athey, Susan, Lisa K. Simon, Oskar N. Skans, Johan Vikström, and Yaroslav Yakymovyc. “The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets.”arXiv preprint arXiv:2307.06684 (2023).

https://doi.org/10.48550/arXiv.2307.06684

Topic(s): Econometric Theory and Machine Learning; Labor Market Transitions; Social Impact

Athey, Susan, and Emil Palikot. “Effective and Scalable Programs to Facilitate Labor Market Transitions for Women in Technology.” arXiv preprint arXiv:2211.09968 (2022).

https://doi.org/10.48550/arXiv.2211.09968

Topic(s): Labor Market Transitions; Social Impact