Victor Veitch

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Invariant Representation Learning for Treatment Effect Estimation

The defining challenge for causal inference from observational data is t...
11/24/2020 ∙ by Claudia Shi, et al. ∙ 8 ∙ share

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Underspecification Presents Challenges for Credibility in Modern Machine Learning

ML models often exhibit unexpectedly poor behavior when they are deploye...
11/06/2020 ∙ by Alexander D'Amour, et al. ∙ 30 ∙ share

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Causal Effects of Linguistic Properties

We consider the problem of estimating the causal effects of linguistic p...
10/24/2020 ∙ by Reid Pryzant, et al. ∙ 0 ∙ share

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Valid Causal Inference with (Some) Invalid Instruments

Instrumental variable methods provide a powerful approach to estimating ...
06/19/2020 ∙ by Jason Hartford, et al. ∙ 8 ∙ share

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

It is a truth universally acknowledged that an observed association with...
03/03/2020 ∙ by Victor Veitch, et al. ∙ 0 ∙ share

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Adapting Neural Networks for the Estimation of Treatment Effects

This paper addresses the use of neural networks for the estimation of tr...
06/05/2019 ∙ by Claudia Shi, et al. ∙ 6 ∙ share

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Using Text Embeddings for Causal Inference

We address causal inference with text documents. For example, does addin...
05/29/2019 ∙ by Victor Veitch, et al. ∙ 0 ∙ share

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Using Embeddings to Correct for Unobserved Confounding

We consider causal inference in the presence of unobserved confounding. ...
02/11/2019 ∙ by Victor Veitch, et al. ∙ 10 ∙ share

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The Holdout Randomization Test: Principled and Easy Black Box Feature Selection

We consider the problem of feature selection using black box predictive ...
11/01/2018 ∙ by Wesley Tansey, et al. ∙ 0 ∙ share

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Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data

Empirical risk minimization is the principal tool for prediction problem...
06/27/2018 ∙ by Victor Veitch, et al. ∙ 0 ∙ share

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Compressibility and Generalization in Large-Scale Deep Learning

Modern neural networks are highly overparameterized, with capacity to su...
04/16/2018 ∙ by Wenda Zhou, et al. ∙ 0 ∙ share

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

A variety of machine learning tasks---e.g., matrix factorization, topic ...
12/06/2017 ∙ by Victor Veitch, et al. ∙ 0 ∙ share

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An estimator for the tail-index of graphex processes

Sparse exchangeable graphs resolve some pathologies in traditional rando...
12/05/2017 ∙ by Zacharie Naulet, et al. ∙ 0 ∙ share

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Victor Veitch

Featured Co-authors

David M. Blei

Andrea Montanari

Ryan P. Adams

Dan Jurafsky

Daniel M. Roy

Jacob Eisenstein

Mario Lucic

Yixin Wang

Neil Houlsby

Kevin Leyton-Brown

David Blei

Invariant Representation Learning for Treatment Effect Estimation

Underspecification Presents Challenges for Credibility in Modern Machine Learning

Causal Effects of Linguistic Properties

Valid Causal Inference with (Some) Invalid Instruments

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

Adapting Neural Networks for the Estimation of Treatment Effects

Using Text Embeddings for Causal Inference

Using Embeddings to Correct for Unobserved Confounding

The Holdout Randomization Test: Principled and Easy Black Box Feature Selection

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data

Compressibility and Generalization in Large-Scale Deep Learning

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

An estimator for the tail-index of graphex processes