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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 Sep 2021.
  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. 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.
    2. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    3. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    4. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Yiyan Huang & Cheuk Hang Leung & Xing Yan & Qi Wu & Nanbo Peng & Dongdong Wang & Zhixiang Huang, 2020. "The Causal Learning of Retail Delinquency," Papers 2012.09448, arXiv.org.
    6. Knaus, Michael C. & Lechner, Michael & anthony.strittmatter@unisg.ch, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Economics Working Paper Series 1817, University of St. Gallen, School of Economics and Political Science.
    7. Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
    8. Rolando Gonzales Martinez, 2021. "How good is good? Probabilistic benchmarks and nanofinance+," Papers 2103.01669, arXiv.org.
    9. AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
    10. Nathan Kallus & Xiaojie Mao, 2023. "Stochastic Optimization Forests," Management Science, INFORMS, vol. 69(4), pages 1975-1994, April.
    11. Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
    12. Yiyan Huang & Cheuk Hang Leung & Qi Wu & Xing Yan, 2021. "Robust Orthogonal Machine Learning of Treatment Effects," Papers 2103.11869, arXiv.org, revised Dec 2022.
    13. Ziwei Cong & Jia Liu & Puneet Manchanda, 2021. "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers 2107.01629, arXiv.org, revised Sep 2022.
    14. Rahul Singh, 2020. "Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments," Papers 2012.10315, arXiv.org, revised Mar 2023.
    15. Ravi Kumar & Shahin Boluki & Karl Isler & Jonas Rauch & Darius Walczak, 2022. "Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing," Papers 2205.01875, arXiv.org, revised Dec 2022.
    16. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.
    17. von Zahn, Moritz & Bauer, Kevin & Mihale-Wilson, Cristina & Jagow, Johanna & Speicher, Max & Hinz, Oliver, 2022. "The smart green nudge: Reducing product returns through enriched digital footprints & causal machine learning," SAFE Working Paper Series 363, Leibniz Institute for Financial Research SAFE, revised 2022.
    18. Xiao Liu, 2023. "Dynamic Coupon Targeting Using Batch Deep Reinforcement Learning: An Application to Livestream Shopping," Marketing Science, INFORMS, vol. 42(4), pages 637-658, July.
    19. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    20. 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.
    21. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    22. Jann Spiess & Vasilis Syrgkanis & Victor Yaneng Wang, 2021. "Finding Subgroups with Significant Treatment Effects," Papers 2103.07066, arXiv.org, revised Dec 2023.

  2. Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models," Papers 1806.04823, arXiv.org, revised Sep 2021.

    Cited by:

    1. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.
    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. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.

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

    Cited by:

    1. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Source Condition Double Robust Inference on Functionals of Inverse Problems," Papers 2307.13793, arXiv.org.
    2. Ziyu Wang & Yucen Luo & Yueru Li & Jun Zhu & Bernhard Scholkopf, 2022. "Spectral Representation Learning for Conditional Moment Models," Papers 2210.16525, arXiv.org, revised Dec 2022.
    3. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness," Papers 2302.05404, arXiv.org.
    4. Andrew Bennett & Nathan Kallus, 2020. "The Variational Method of Moments," Papers 2012.09422, arXiv.org, revised Mar 2023.
    5. Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
    6. Jason Hartford & Victor Veitch & Dhanya Sridhar & Kevin Leyton-Brown, 2020. "Valid Causal Inference with (Some) Invalid Instruments," Papers 2006.11386, arXiv.org.
    7. Zihao Li & Hui Lan & Vasilis Syrgkanis & Mengdi Wang & Masatoshi Uehara, 2024. "Regularized DeepIV with Model Selection," Papers 2403.04236, arXiv.org.
    8. Krikamol Muandet & Arash Mehrjou & Si Kai Lee & Anant Raj, 2019. "Dual Instrumental Variable Regression," Papers 1910.12358, arXiv.org, revised Oct 2020.
    9. Zhang Rui & Imaizumi Masaaki & Schölkopf Bernhard & Muandet Krikamol, 2023. "Instrumental variable regression via kernel maximum moment loss," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-42, January.
    10. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.
    11. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    12. Zhaonan Qu & Yongchan Kwon, 2024. "Distributionally Robust Instrumental Variables Estimation," Papers 2410.15634, arXiv.org.
    13. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.

  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. Yiyan Huang & Cheuk Hang Leung & Xing Yan & Qi Wu & Nanbo Peng & Dongdong Wang & Zhixiang Huang, 2020. "The Causal Learning of Retail Delinquency," Papers 2012.09448, arXiv.org.
    2. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
    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. Jacob Dorn & Kevin Guo & Nathan Kallus, 2021. "Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding," Papers 2112.11449, arXiv.org, revised Jul 2022.
    5. Yiyan Huang & Cheuk Hang Leung & Xing Yan & Qi Wu & Shumin Ma & Zhiri Yuan & Dongdong Wang & Zhixiang Huang, 2022. "Robust Causal Learning for the Estimation of Average Treatment Effects," Papers 2209.01805, arXiv.org.
    6. Yiyan Huang & Cheuk Hang Leung & Qi Wu & Xing Yan, 2021. "Robust Orthogonal Machine Learning of Treatment Effects," Papers 2103.11869, arXiv.org, revised Dec 2022.
    7. Ravi Kumar & Shahin Boluki & Karl Isler & Jonas Rauch & Darius Walczak, 2022. "Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing," Papers 2205.01875, arXiv.org, revised Dec 2022.
    8. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.
    9. Jiafeng Chen & Daniel L. Chen & Greg Lewis, 2020. "Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models," Papers 2011.06158, arXiv.org, revised Jun 2021.
    10. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    11. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    12. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.

  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," Cowles Foundation Discussion Papers 2075R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2017.
    2. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    3. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    4. Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2019. "Counterfactuals with Latent Information," Cowles Foundation Discussion Papers 2162R3, Cowles Foundation for Research in Economics, Yale University, revised Aug 2021.
    5. Miltiadis Makris & Ludovic Renou, 2018. "Information design in multi-stage games," Working Papers 861, Queen Mary University of London, School of Economics and Finance.
    6. Giovanni Compiani & Philip Haile & Marcelo Sant’Anna, 2020. "Common Values, Unobserved Heterogeneity, and Endogenous Entry in US Offshore Oil Lease Auctions," Journal of Political Economy, University of Chicago Press, vol. 128(10), pages 3872-3912.
    7. Fabian Dunker & Stefan Hoderlein & Hiroaki Kaido, 2023. "Nonparametric identification of random coefficients in aggregate demand models for differentiated products," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 279-306.
    8. 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.
    9. Cristina Gualdani & Shruti Sinha, 2019. "Identification in discrete choice models with imperfect information," Papers 1911.04529, arXiv.org, revised Dec 2023.
    10. Bulat Gafarov, 2019. "Simple subvector inference on sharp identified set in affine models," Papers 1904.00111, arXiv.org, revised Jul 2024.
    11. Laura Doval & Jeffrey C. Ely, 2020. "Sequential Information Design," Econometrica, Econometric Society, vol. 88(6), pages 2575-2608, November.
    12. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.
    13. Makris, Miltiadis & Renou, Ludovic, 2023. "Information design in multi-stage games," Theoretical Economics, Econometric Society, vol. 18(4), November.

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 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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