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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

* 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

* 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

* 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

* 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

* 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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