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Susan Athey

The Economics of Technology Professor, Stanford Graduate School of Business

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Econometric Theory and Machine Learning

Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python

  • Post published:May 2, 2023
  • Post category:

Tianyu Du, Ayush Kanodia, Susan Athey. April 2023 URL: https://arxiv.org/abs/2304.01906

Continue ReadingTorch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python

Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning

  • Post published:November 1, 2022
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Athey, Susan, Undral Byambadalai, Vitor Hadad, Sanath Kumar Krishnamurthy, Weiwen Leung, Joseph Jay Williams. arXiv preprint arXiv:2211:12004 (2022). URL: https://doi.org/10.48550/arXiv.2211.12004

Continue ReadingContextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning

CAREER: Transfer Learning for Economic Prediction of Labor Sequence Data

  • Post published:October 1, 2022
  • Post category:

Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei October 2022 PDF: https://www.gsb.stanford.edu/gsb-box/route-download/620742

Continue ReadingCAREER: Transfer Learning for Economic Prediction of Labor Sequence Data

Uncovering Interpretable Potential Confounders in Electronic Medical Records

  • Post published:February 23, 2022
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Jiaming Zeng, Michael F. Gensheimer, Daniel L. Rubin, Susan Athey, Ross D. Shachter Nature Communications February 23, 2022 Vol. 13 URL: https://doi.org/10.1038/s41467-022-28546-8

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Stable Learning Establishes Some Common Ground between Causal Inference and Machine Learning

  • Post published:February 1, 2022
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Peng Cui, Susan Athey, Nature Machine Intelligence, February 2022 Vol. 4 Issue 2 Pages 110–115 URL: https://doi.org/10.1038/s42256-022-00445-z

Continue ReadingStable Learning Establishes Some Common Ground between Causal Inference and Machine Learning

Synthetic Difference-in-Differences

  • Post published:December 1, 2021
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Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, Stefan Wager. American Economic Review. December 2021 Vol. 111 Issue 12 Pages 4088–4118 URL: https://www.aeaweb.org/articles?id=10.1257/aer.20190159

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Semiparametric Estimation of Treatment Effects in Randomized Experiments

  • Post published:September 1, 2021
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Athey, Susan, Peter J. Bickel, Aiyou Chen, Guido Imbens, and Michael Pollmann. National Bureau of Economic Research Working Paper No. w29242. (2021) URL: https://www.nber.org/papers/w29242 

Continue ReadingSemiparametric Estimation of Treatment Effects in Randomized Experiments

Breiman’s Two Cultures – A Perspective from Econometrics

  • Post published:July 1, 2021
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Guido Imbens Observational Studies, Volume 7, Issue 1, July 2021 July 2021 URL: https://muse.jhu.edu/article/799753

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Federated Causal Inference in Heterogeneous Observational Data

  • Post published:July 1, 2021
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Ruoxuan Xiong, Allison Koenecke, Michael Powell, Zhu Shen, Joshua T. Vogelstein arXiv:2107.11732 August 2021 URL: https://arxiv.org/abs/2107.11732

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Integrating explanation and prediction in computational social science

  • Post published:June 1, 2021
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Jake M. Hofman, Duncan J. Watts Nature (2021). https://doi.org/10.1038/s41586-021-03659-0. June 2021 URL: https://www.nature.com/articles/s41586-021-03659-0

Continue ReadingIntegrating explanation and prediction in computational social science
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