Alexander D'Amour | DeepAI

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∙ 09/14/2023

Choosing a Proxy Metric from Past Experiments

In many randomized experiments, the treatment effect of the long-term me...

0 Nilesh Tripuraneni, et al. ∙

∙ 03/03/2023

When does Privileged Information Explain Away Label Noise?

Leveraging privileged information (PI), or features available during tra...

0 Guillermo Ortiz-Jiménez, et al. ∙

∙ 12/21/2022

Adapting to Latent Subgroup Shifts via Concepts and Proxies

We address the problem of unsupervised domain adaptation when the source...

1 Ibrahim Alabdulmohsin, et al. ∙

∙ 11/28/2022

Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations

Spurious correlations, or correlations that change across domains where ...

0 Qingyao Sun, et al. ∙

∙ 09/20/2022

Fairness and robustness in anti-causal prediction

Robustness to distribution shift and fairness have independently emerged...

0 Maggie Makar, et al. ∙

∙ 08/13/2022

Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes

We propose an approach for assessing sensitivity to unobserved confoundi...

0 Jiajing Zheng, et al. ∙

∙ 05/21/2022

Bias-robust Integration of Observational and Experimental Estimators

We describe a simple approach for combining an unbiased and a (possibly)...

0 Michael Oberst, et al. ∙

∙ 02/02/2022

Maintaining fairness across distribution shift: do we have viable solutions for real-world applications?

Fairness and robustness are often considered as orthogonal dimensions wh...

31 Jessica Schrouff, et al. ∙

∙ 11/15/2021

Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding

In causal estimation problems, the parameter of interest is often only p...

0 Jiajing Zheng, et al. ∙

∙ 06/30/2021

The MultiBERTs: BERT Reproductions for Robustness Analysis

Experiments with pretrained models such as BERT are often based on a sin...

0 Thibault Sellam, et al. ∙

∙ 05/31/2021

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

Informally, a `spurious correlation' is the dependence of a model on som...

0 Victor Veitch, et al. ∙

∙ 05/13/2021

Causally-motivated Shortcut Removal Using Auxiliary Labels

Robustness to certain distribution shifts is a key requirement in many M...

0 Maggie Makar, et al. ∙

∙ 04/12/2021

Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap

A key condition for obtaining reliable estimates of the causal effect of...

0 Alexander D'Amour, et al. ∙

∙ 04/05/2021

Revisiting Rashomon: A Comment on "The Two Cultures"

Here, I provide some reflections on Prof. Leo Breiman's "The Two Culture...

0 Alexander D'Amour, et al. ∙

∙ 03/23/2021

SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression

Logistic regression remains one of the most widely used tools in applied...

0 Steve Yadlowsky, et al. ∙

∙ 02/18/2021

Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

Recent work has focused on the potential and pitfalls of causal identifi...

0 Jiajing Zheng, et al. ∙

∙ 11/06/2020

Underspecification Presents Challenges for Credibility in Modern Machine Learning

ML models often exhibit unexpectedly poor behavior when they are deploye...

30 Alexander D'Amour, et al. ∙

∙ 07/16/2020

On Robustness and Transferability of Convolutional Neural Networks

Modern deep convolutional networks (CNNs) are often criticized for not g...

15 Josip Djolonga, et al. ∙

∙ 06/19/2020

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Covariate shift has been shown to sharply degrade both predictive accura...

10 Zachary Nado, et al. ∙

∙ 11/11/2019

A Biologically Plausible Benchmark for Contextual Bandit Algorithms in Precision Oncology Using in vitro Data

Precision oncology, the genetic sequencing of tumors to identify druggab...

0 Niklas T. Rindtorff, et al. ∙

∙ 10/17/2019

Comment: Reflections on the Deconfounder

The aim of this comment (set to appear in a formal discussion in JASA) i...

0 Alexander D'Amour, et al. ∙

∙ 02/27/2019

On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives

Unobserved confounding is a central barrier to drawing causal inferences...

0 Alexander D'Amour, et al. ∙

∙ 09/02/2018

Flexible sensitivity analysis for observational studies without observable implications

A fundamental challenge in observational causal inference is that assump...

0 Alexander Franks, et al. ∙

∙ 11/07/2017

Overlap in Observational Studies with High-Dimensional Covariates

Causal inference in observational settings typically rests on a pair of ...

0 Alexander D'Amour, et al. ∙

∙ 05/22/2017

Reducing Reparameterization Gradient Variance

Optimization with noisy gradients has become ubiquitous in statistics an...

0 Andrew C. Miller, et al. ∙

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