
Invariant Representation Learning for Treatment Effect Estimation
The defining challenge for causal inference from observational data is t...
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Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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Causal Effects of Linguistic Properties
We consider the problem of estimating the causal effects of linguistic p...
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Valid Causal Inference with (Some) Invalid Instruments
Instrumental variable methods provide a powerful approach to estimating ...
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Sense and Sensitivity Analysis: Simple PostHoc Analysis of Bias Due to Unobserved Confounding
It is a truth universally acknowledged that an observed association with...
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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...
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Using Text Embeddings for Causal Inference
We address causal inference with text documents. For example, does addin...
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Using Embeddings to Correct for Unobserved Confounding
We consider causal inference in the presence of unobserved confounding. ...
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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 ...
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Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Empirical risk minimization is the principal tool for prediction problem...
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Compressibility and Generalization in LargeScale Deep Learning
Modern neural networks are highly overparameterized, with capacity to su...
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Exchangeable modelling of relational data: checking sparsity, traintest splitting, and sparse exchangeable Poisson matrix factorization
A variety of machine learning taskse.g., matrix factorization, topic ...
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An estimator for the tailindex of graphex processes
Sparse exchangeable graphs resolve some pathologies in traditional rando...
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Victor Veitch
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