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.eduUSA, Ithaca
Research output
Jump to: Working papersWorking papers
- Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
- Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models," Papers 1806.04823, arXiv.org, revised Sep 2021.
- Greg Lewis & Vasilis Syrgkanis, 2018. "Adversarial Generalized Method of Moments," Papers 1803.07164, arXiv.org, revised Apr 2018.
- Lester Mackey & Vasilis Syrgkanis & Ilias Zadik, 2017. "Orthogonal Machine Learning: Power and Limitations," Papers 1711.00342, arXiv.org, revised Aug 2018.
- 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
- Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018.
"Orthogonal Random Forest for Causal Inference,"
Papers
1806.03467, arXiv.org, revised Sep 2019.
Cited by:
- Lechner, Michael, 2019.
"Modified Causal Forests for Estimating Heterogeneous Causal Effects,"
CEPR Discussion Papers
13430, C.E.P.R. Discussion Papers.
- Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Economics Working Paper Series 1901, University of St. Gallen, School of Economics and Political Science.
- Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
- Michael Lechner, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Papers 1812.09487, arXiv.org, revised Jul 2019.
- Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
- 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.
- Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018.
"Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence,"
CEPR Discussion Papers
13402, C.E.P.R. Discussion Papers.
- Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Papers 1810.13237, arXiv.org, revised Dec 2018.
- Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023.
"Targeting predictors in random forest regression,"
International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
- 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.
- 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.
- Rahul Singh, 2020. "Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments," Papers 2012.10315, arXiv.org, revised Mar 2023.
- Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.
- 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.
- 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.
- 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.
- 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.
- Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
- Jann Spiess & Vasilis Syrgkanis & Victor Yaneng Wang, 2021. "Finding Subgroups with Significant Treatment Effects," Papers 2103.07066, arXiv.org, revised Dec 2023.
- Rolando Gonzales Martinez, 2021. "How good is good? Probabilistic benchmarks and nanofinance+," Papers 2103.01669, arXiv.org.
- Nathan Kallus & Xiaojie Mao, 2023. "Stochastic Optimization Forests," Management Science, INFORMS, vol. 69(4), pages 1975-1994, April.
- 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.
- Lechner, Michael, 2019.
"Modified Causal Forests for Estimating Heterogeneous Causal Effects,"
CEPR Discussion Papers
13430, C.E.P.R. Discussion Papers.
- 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:
- Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
- Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
- Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.
- Greg Lewis & Vasilis Syrgkanis, 2018.
"Adversarial Generalized Method of Moments,"
Papers
1803.07164, arXiv.org, revised Apr 2018.
Cited by:
- Krikamol Muandet & Arash Mehrjou & Si Kai Lee & Anant Raj, 2019. "Dual Instrumental Variable Regression," Papers 1910.12358, arXiv.org, revised Oct 2020.
- 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.
- Jason Hartford & Victor Veitch & Dhanya Sridhar & Kevin Leyton-Brown, 2020. "Valid Causal Inference with (Some) Invalid Instruments," Papers 2006.11386, arXiv.org.
- Luyang Chen & Markus Pelger & Jason Zhu, 2019.
"Deep Learning in Asset Pricing,"
Papers
1904.00745, arXiv.org, revised Aug 2021.
- Luyang Chen & Markus Pelger & Jason Zhu, 2024. "Deep Learning in Asset Pricing," Management Science, INFORMS, vol. 70(2), pages 714-750, February.
- Andrew Bennett & Nathan Kallus, 2020. "The Variational Method of Moments," Papers 2012.09422, arXiv.org, revised Mar 2023.
- Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
- Zihao Li & Hui Lan & Vasilis Syrgkanis & Mengdi Wang & Masatoshi Uehara, 2024. "Regularized DeepIV with Model Selection," Papers 2403.04236, arXiv.org.
- 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.
- Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
- 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.
- 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.
- 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.
- Lester Mackey & Vasilis Syrgkanis & Ilias Zadik, 2017.
"Orthogonal Machine Learning: Power and Limitations,"
Papers
1711.00342, arXiv.org, revised Aug 2018.
Cited by:
- 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.
- Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
- Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
- Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
- 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.
- Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.
- 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.
- 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.
- Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
- 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.
- 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.
- 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.
- 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:
- Dirk Bergemann & Stephen Morris, 2019.
"Information Design: A Unified Perspective,"
Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
- Dirk Bergemann & Stephen Morris, 2017. "Information Design: A Unified Perspective," Cowles Foundation Discussion Papers 2075, Cowles Foundation for Research in Economics, Yale University.
- Bergemann, Dirk & Morris, Stephen, 2017. "Information Design: A Unified Perspective," CEPR Discussion Papers 11867, C.E.P.R. Discussion Papers.
- Dirk Bergemann & Stephen Morris, 2017. "Information Design: A Unified Perspective," Cowles Foundation Discussion Papers 2075R3, Cowles Foundation for Research in Economics, Yale University, revised Mar 2018.
- Dirk Bergemann & Stephen Morris, 2017. "Information Design: A Unified Perspective," Working Papers 089_2017, Princeton University, Department of Economics, Econometric Research Program..
- 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.
- Dirk Bergemann & Stephen Morris, 2017. "Information Design: A Unified Perspective," Cowles Foundation Discussion Papers 2075R2, Cowles Foundation for Research in Economics, Yale University, revised Nov 2017.
- 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.
- 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.
- 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.
- Giovanni Compiani & Philip Haile & Marcelo Sant'Anna, 2018. "Common Values, Unobserved Heterogeneity, and Endogenous Entry in U.S. Offshore Oil Lease Auction," NBER Working Papers 24795, National Bureau of Economic Research, Inc.
- 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.
- 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.
- 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.
- Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2019. "Counterfactuals with Latent Information," Cowles Foundation Discussion Papers 2162R4, Cowles Foundation for Research in Economics, Yale University, revised Oct 2021.
- Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2019. "Counterfactuals with Latent Information," Cowles Foundation Discussion Papers 2162, Cowles Foundation for Research in Economics, Yale University.
- Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2019. "Counterfactuals with Latent Information," Cowles Foundation Discussion Papers 2162R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2019.
- Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2022. "Counterfactuals with Latent Information," American Economic Review, American Economic Association, vol. 112(1), pages 343-368, January.
- Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2019. "Counterfactuals with Latent Information," Cowles Foundation Discussion Papers 2162R2, Cowles Foundation for Research in Economics, Yale University, revised Mar 2021.
- Miltiadis Makris & Ludovic Renou, 2021.
"Information Design in Multi-stage Games,"
Papers
2102.13482, arXiv.org, revised Apr 2021.
- Miltiadis Makris & Ludovic Renou, 2018. "Information design in multi-stage games," Working Papers 861, Queen Mary University of London, School of Economics and Finance.
- Francesca Molinari, 2020.
"Microeconometrics with Partial Identi?cation,"
CeMMAP working papers
CWP15/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
- 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.
- Cristina Gualdani & Shruti Sinha, 2019. "Identification in discrete choice models with imperfect information," Papers 1911.04529, arXiv.org, revised Dec 2023.
- Bulat Gafarov, 2019. "Simple subvector inference on sharp identified set in affine models," Papers 1904.00111, arXiv.org, revised Jul 2024.
- Laura Doval & Jeffrey C. Ely, 2020. "Sequential Information Design," Econometrica, Econometric Society, vol. 88(6), pages 2575-2608, November.
- Makris, Miltiadis & Renou, Ludovic, 2023. "Information design in multi-stage games," Theoretical Economics, Econometric Society, vol. 18(4), November.
- Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
- Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.
- Dirk Bergemann & Stephen Morris, 2019.
"Information Design: A Unified Perspective,"
Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
More information
Research fields, statistics, top rankings, if available.Statistics
Access and download statistics for all items
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.- NEP-ECM: Econometrics (5) 2017-10-15 2017-12-03 2018-04-09 2018-06-25 2018-07-16. Author is listed
- NEP-BIG: Big Data (3) 2017-12-03 2018-04-09 2018-07-16. Author is listed
- NEP-CMP: Computational Economics (2) 2017-10-15 2017-12-03. Author is listed
- NEP-DES: Economic Design (1) 2017-10-15
- NEP-GTH: Game Theory (1) 2017-10-15
Corrections
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