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Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

Sep 02, 2021
Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang

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Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests

Jun 02, 2021
Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein

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Invariant Representation Learning for Treatment Effect Estimation

Nov 24, 2020
Claudia Shi, Victor Veitch, David Blei

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Underspecification Presents Challenges for Credibility in Modern Machine Learning

Nov 06, 2020
Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley

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Causal Effects of Linguistic Properties

Oct 24, 2020
Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar

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Valid Causal Inference with (Some) Invalid Instruments

Jun 19, 2020
Jason Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown

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Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding

Mar 03, 2020
Victor Veitch, Anisha Zaveri

* "Austen" is Jane Austen, in service of the pun in the title 

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Adapting Neural Networks for the Estimation of Treatment Effects

Jun 05, 2019
Claudia Shi, David M. Blei, Victor Veitch

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Using Text Embeddings for Causal Inference

May 29, 2019
Victor Veitch, Dhanya Sridhar, David M. Blei

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Using Embeddings to Correct for Unobserved Confounding

Feb 11, 2019
Victor Veitch, Yixin Wang, David M. Blei

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Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data

Jun 27, 2018
Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz

* 23 pages, 1 figure 

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Compressibility and Generalization in Large-Scale Deep Learning

May 21, 2018
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

* 14 pages, 1 figure. Minor phrasing changes and better notation for proofs 

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Exchangeable modelling of relational data: checking sparsity, train-test splitting, and sparse exchangeable Poisson matrix factorization

Dec 06, 2017
Victor Veitch, Ekansh Sharma, Zacharie Naulet, Daniel M. Roy

* 9 pages, 4 figures 

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