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Vasilis Syrgkanis

Personal Details

First Name:Vasilis
Middle Name:
Last Name:Syrgkanis
Suffix:
RePEc Short-ID:psy44
http://www.cs.cornell.edu/~vasilis

Affiliation

Cornell University, Department of Computer Science (Cornell University, Department of Computer Science)

http://www.cs.cornell.edu
USA, Ithaca

Research output

as
Jump to: Working papers

Working papers

  1. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
  2. Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models," Papers 1806.04823, arXiv.org, revised Oct 2020.
  3. Greg Lewis & Vasilis Syrgkanis, 2018. "Adversarial Generalized Method of Moments," Papers 1803.07164, arXiv.org, revised Apr 2018.
  4. Lester Mackey & Vasilis Syrgkanis & Ilias Zadik, 2017. "Orthogonal Machine Learning: Power and Limitations," Papers 1711.00342, arXiv.org, revised Aug 2018.
  5. Vasilis Syrgkanis & Elie Tamer & Juba Ziani, 2017. "Inference on Auctions with Weak Assumptions on Information," Papers 1710.03830, arXiv.org, revised Mar 2018.

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. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.

    Cited by:

    1. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    2. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Oct 2020.
    3. Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
    4. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," IZA Discussion Papers 12039, Institute of Labor Economics (IZA).
    5. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Sep 2020.
    7. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning," Papers 2010.04855, arXiv.org, revised Oct 2020.
    8. Gubela, Robin M. & Lessmann, Stefan & Jaroszewicz, Szymon, 2020. "Response transformation and profit decomposition for revenue uplift modeling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 647-661.

  2. Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models," Papers 1806.04823, arXiv.org, revised Oct 2020.

    Cited by:

    1. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    2. Sookyo Jeong & Hongseok Namkoong, 2020. "Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects," Papers 2007.02411, arXiv.org, revised Jul 2020.
    3. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Sep 2020.

  3. Greg Lewis & Vasilis Syrgkanis, 2018. "Adversarial Generalized Method of Moments," Papers 1803.07164, arXiv.org, revised Apr 2018.

    Cited by:

    1. Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
    2. Krikamol Muandet & Arash Mehrjou & Si Kai Lee & Anant Raj, 2019. "Dual Instrumental Variable Regression," Papers 1910.12358, arXiv.org, revised Oct 2020.
    3. Jason Hartford & Victor Veitch & Dhanya Sridhar & Kevin Leyton-Brown, 2020. "Valid Causal Inference with (Some) Invalid Instruments," Papers 2006.11386, arXiv.org.
    4. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised May 2020.

  4. Lester Mackey & Vasilis Syrgkanis & Ilias Zadik, 2017. "Orthogonal Machine Learning: Power and Limitations," Papers 1711.00342, arXiv.org, revised Aug 2018.

    Cited by:

    1. Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
    2. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    3. Sookyo Jeong & Hongseok Namkoong, 2020. "Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects," Papers 2007.02411, arXiv.org, revised Jul 2020.
    4. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org.
    5. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Sep 2020.

  5. Vasilis Syrgkanis & Elie Tamer & Juba Ziani, 2017. "Inference on Auctions with Weak Assumptions on Information," Papers 1710.03830, arXiv.org, revised Mar 2018.

    Cited by:

    1. Dirk Bergemann & Stephen Morris, 2017. "Information Design: A Unified Perspective," Working Papers 089_2017, Princeton University, Department of Economics, Econometric Research Program..
    2. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    3. Gualdani, Cristina & Sinha, Shruti, 2019. "Identification and inference in discrete choice models with imperfect information," TSE Working Papers 19-1049, Toulouse School of Economics (TSE), revised Jun 2020.
    4. Cristina Gualdani & Shruti Sinha, 2019. "Identification and inference in discrete choice models with imperfect information," Papers 1911.04529, arXiv.org, revised Jul 2020.
    5. Bulat Gafarov, 2019. "Inference in high-dimensional set-identified affine models," Papers 1904.00111, arXiv.org.
    6. Giovanni Compiani & Phil Haile & Marcelo Sant'Anna, 2018. "Common values, unobserved heterogeneity, and endogenous entry in U.S. offshore oil lease auctions," CeMMAP working papers CWP37/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2019. "Counterfactuals with Latent Information," Cowles Foundation Discussion Papers 2162, Cowles Foundation for Research in Economics, Yale University.
    8. Giovanni Compiani & Philip A. Haile & Marcelo Sant'Anna, 2018. "Common Values, Unobserved Heterogeneity, and Endogenous Entry in U.S. Offshore Oil Lease Auctions," Cowles Foundation Discussion Papers 2137, Cowles Foundation for Research in Economics, Yale University.
    9. Giovanni Compiani & Philip A. Haile & Marcelo Sant'Anna, 2018. "Common Values, Unobserved Heterogeneity, and Endogenous Entry in U.S. Offshore Oil Lease Auctions," Cowles Foundation Discussion Papers 2137R, Cowles Foundation for Research in Economics, Yale University, revised Jun 2019.

More information

Research fields, statistics, top rankings, if available.

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 5 papers 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-ECM: Econometrics (5) 2017-10-15 2017-12-03 2018-04-09 2018-06-25 2018-07-16. Author is listed
  2. NEP-BIG: Big Data (3) 2017-12-03 2018-04-09 2018-07-16. Author is listed
  3. NEP-CMP: Computational Economics (2) 2017-10-15 2017-12-03. Author is listed
  4. NEP-DES: Economic Design (1) 2017-10-15
  5. NEP-GTH: Game Theory (1) 2017-10-15

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