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Robert Donnelly

Personal Details

First Name:Robert
Middle Name:
Last Name:Donnelly
Suffix:
RePEc Short-ID:pdo362
[This author has chosen not to make the email address public]
Terminal Degree:2019 Graduate School of Business; Stanford University (from RePEc Genealogy)

Research output

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Jump to: Working papers Articles

Working papers

  1. Susan Athey & David Blei & Robert Donnelly & Francisco Ruiz & Tobias Schmidt, 2018. "Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data," Papers 1801.07826, arXiv.org.

Articles

  1. Robert Donnelly & Ayush Kanodia & Ilya Morozov, 2024. "Welfare Effects of Personalized Rankings," Marketing Science, INFORMS, vol. 43(1), pages 92-113, January.
  2. Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.
  3. Robert Donnelly & Francisco J. R. Ruiz & David Blei & Susan Athey, 2021. "Correction to: Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 409-409, December.
  4. Susan Athey & David Blei & Robert Donnelly & Francisco Ruiz & Tobias Schmidt, 2018. "Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 64-67, May.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Susan Athey & David Blei & Robert Donnelly & Francisco Ruiz & Tobias Schmidt, 2018. "Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data," Papers 1801.07826, arXiv.org.

    Cited by:

    1. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    2. Krueger, Rico & Bierlaire, Michel & Daziano, Ricardo A. & Rashidi, Taha H. & Bansal, Prateek, 2021. "Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity," Journal of choice modelling, Elsevier, vol. 41(C).
    3. Patacchini, Eleonora & Barwick, Panle Jia & Liu, Yanyan & Wu, Qi, 2019. "Information, Mobile Communication, and Referral Effects," CEPR Discussion Papers 13786, C.E.P.R. Discussion Papers.
    4. Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.
    5. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Apr 2024.
    6. Badruddoza, Syed & Amin, Modhurima & McCluskey, Jill, 2019. "Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning," Working Papers 2019-5, School of Economic Sciences, Washington State University.
    7. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
    8. Xie, Lusi & Adamowicz, Wiktor & Lloyd-Smith, Patrick, 2023. "Spatial and temporal responses to incentives: An application to wildlife disease management," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    9. Du, Tianyu & Kanodia, Ayush & Athey, Susan, 2023. "Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python," Research Papers 4106, Stanford University, Graduate School of Business.
    10. J. Daniel Aromí & M. Paula Bonel & Julián Cristiá & Martín Llada, 2020. "Socio-economic status and mobility during the COVID-19 pandemic: An analysis of large Latin American urban areas," Asociación Argentina de Economía Política: Working Papers 4307, Asociación Argentina de Economía Política.
    11. Gaubert, Cécile & Couture, Victor & Handbury, Jessie & Hurst, Erik, 2020. "Income Growth and the Distributional Effects of Urban Spatial Sorting," CEPR Discussion Papers 14350, C.E.P.R. Discussion Papers.
    12. Evan Munro & Serena Ng, 2022. "Latent Dirichlet Analysis of Categorical Survey Responses," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 256-271, January.
    13. Gabriel E. Kreindler & Yuhei Miyauchi, 2019. "Measuring Commuting and Economic Activity inside Cities with Cell Phone Records," Boston University - Department of Economics - Working Papers Series WP2020-006, Boston University - Department of Economics, revised Apr 2020.
    14. Federica Daniele & Mariona Segu & David Bounie & Youssouf Camara, 2022. "Bike-friendly cities: an opportunity for local businesses? Evidence from the city of Paris," THEMA Working Papers 2022-09, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

Articles

  1. Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.

    Cited by:

    1. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2023. "Optimal Price Targeting," Marketing Science, INFORMS, vol. 42(3), pages 476-499, May.
    2. Krueger, Rico & Bierlaire, Michel & Daziano, Ricardo A. & Rashidi, Taha H. & Bansal, Prateek, 2021. "Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity," Journal of choice modelling, Elsevier, vol. 41(C).
    3. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    4. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
    5. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    6. Adam N. Smith & Jim E. Griffin, 2023. "Shrinkage priors for high-dimensional demand estimation," Quantitative Marketing and Economics (QME), Springer, vol. 21(1), pages 95-146, March.
    7. Du, Tianyu & Kanodia, Ayush & Athey, Susan, 2023. "Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python," Research Papers 4106, Stanford University, Graduate School of Business.
    8. Adair Morse & Karen M. Pence, 2020. "Technological Innovation and Discrimination in Household Finance," Finance and Economics Discussion Series 2020-018, Board of Governors of the Federal Reserve System (U.S.).
    9. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2021. "Optimal Price Targeting," CESifo Working Paper Series 9439, CESifo.

  2. Robert Donnelly & Francisco J. R. Ruiz & David Blei & Susan Athey, 2021. "Correction to: Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 409-409, December.

    Cited by:

    1. Yiyan Huang & Cheuk Hang Leung & Siyi Wang & Yijun Li & Qi Wu, 2024. "Unveiling the Potential of Robustness in Evaluating Causal Inference Models," Papers 2402.18392, arXiv.org.
    2. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2023. "Optimal Price Targeting," Marketing Science, INFORMS, vol. 42(3), pages 476-499, May.
    3. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    4. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
    5. Adam N. Smith & Jim E. Griffin, 2023. "Shrinkage priors for high-dimensional demand estimation," Quantitative Marketing and Economics (QME), Springer, vol. 21(1), pages 95-146, March.
    6. Du, Tianyu & Kanodia, Ayush & Athey, Susan, 2023. "Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python," Research Papers 4106, Stanford University, Graduate School of Business.

  3. Susan Athey & David Blei & Robert Donnelly & Francisco Ruiz & Tobias Schmidt, 2018. "Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 64-67, May.
    See citations under working paper version above.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-MKT: Marketing (1) 2018-03-05. Author is listed
  2. NEP-PAY: Payment Systems and Financial Technology (1) 2018-03-05. Author is listed
  3. NEP-TRE: Transport Economics (1) 2018-03-05. Author is listed
  4. NEP-TUR: Tourism Economics (1) 2018-03-05. Author is listed

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