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Vasilis Syrgkanis

Debiased Machine Learning without Sample-Splitting for Stable Estimators

Qizhao Chen , Vasilis Syrgkanis , Morgane Austern

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Towards efficient representation identification in supervised learning

Kartik Ahuja , Divyat Mahajan , Vasilis Syrgkanis , Ioannis Mitliagkas

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* Proceedings of the First Conference on Causal Learning and Reasoning

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Automatic Debiased Machine Learning for Dynamic Treatment Effects

Victor Chernozhukov , Whitney Newey , Rahul Singh , Vasilis Syrgkanis

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Omitted Variable Bias in Machine Learned Causal Models

Victor Chernozhukov , Carlos Cinelli , Whitney Newey , Amit Sharma , Vasilis Syrgkanis

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* This version of the paper was prepared for the NeurIPS-2021 Workshop "Causal Inference & Machine Learning: Why now?''; 32 pages; 4 figures; typos corrected

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Robust Generalized Method of Moments: A Finite Sample Viewpoint

Dhruv Rohatgi , Vasilis Syrgkanis

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* 24 pages, 1 figure

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RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests

Victor Chernozhukov , Whitney K. Newey , Victor Quintas-Martinez , Vasilis Syrgkanis

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DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

Amit Sharma , Vasilis Syrgkanis , Cheng Zhang , Emre Kıcıman

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* Presented at ICML 2021 Workshop on the Neglected Assumptions in Causal Inference(NACI)

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Incentivizing Compliance with Algorithmic Instruments

Daniel Ngo , Logan Stapleton , Vasilis Syrgkanis , Zhiwei Steven Wu

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* In Proceedings of the Thirty-eighth International Conference on Machine Learning (ICML 2021), 17 pages of main text, 53 pages total, 3 figures

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