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Vasilis Syrgkanis

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  • May 2, 2024 | arxiv.org | Vasilis Syrgkanis

    arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

  • Feb 22, 2024 | arxiv.org | Vasilis Syrgkanis

    [Submitted on 22 Feb 2024 (v1), last revised 2 Mar 2024 (this version, v2)] Title:Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation Download a PDF of the paper titled Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation, by Jikai Jin and Vasilis Syrgkanis Download PDF HTML (experimental) Abstract:Average treatment effect estimation is the most central problem in causal inference with application to numerous disciplines.

  • Dec 26, 2023 | arxiv.org | Zhiwei Steven |Keegan Harris |Anish Agarwal |Vasilis Syrgkanis

    [Submitted on 26 Dec 2023 ( v1 ), last revised 13 Feb 2024 (this version, v2)] Title:Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration Download a PDF of the paper titled Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration, by Daniel Ngo and 4 other authors Download PDF HTML (experimental) Abstract:We consider the setting of synthetic control methods (SCMs), a canonical approach used to estimate the...

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