Alexander D'Amour

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berkeley college

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

A key condition for obtaining reliable estimates of the causal effect of...
04/12/2021 ∙ by Alexander D'Amour, et al. ∙ 0 ∙ share

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Revisiting Rashomon: A Comment on "The Two Cultures"

Here, I provide some reflections on Prof. Leo Breiman's "The Two Culture...
04/05/2021 ∙ by Alexander D'Amour, et al. ∙ 0 ∙ share

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

Logistic regression remains one of the most widely used tools in applied...
03/23/2021 ∙ by Steve Yadlowsky, et al. ∙ 0 ∙ share

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

Recent work has focused on the potential and pitfalls of causal identifi...
02/18/2021 ∙ by Jiajing Zheng, et al. ∙ 0 ∙ 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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On Robustness and Transferability of Convolutional Neural Networks

Modern deep convolutional networks (CNNs) are often criticized for not g...
07/16/2020 ∙ by Josip Djolonga, et al. ∙ 15 ∙ share

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Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Covariate shift has been shown to sharply degrade both predictive accura...
06/19/2020 ∙ by Zachary Nado, et al. ∙ 10 ∙ share

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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...
11/11/2019 ∙ by Niklas T. Rindtorff, et al. ∙ 0 ∙ share

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Comment: Reflections on the Deconfounder

The aim of this comment (set to appear in a formal discussion in JASA) i...
10/17/2019 ∙ by Alexander D'Amour, et al. ∙ 0 ∙ share

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

Unobserved confounding is a central barrier to drawing causal inferences...
02/27/2019 ∙ by Alexander D'Amour, et al. ∙ 0 ∙ share

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Flexible sensitivity analysis for observational studies without observable implications

A fundamental challenge in observational causal inference is that assump...
09/02/2018 ∙ by Alexander Franks, et al. ∙ 0 ∙ share

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Overlap in Observational Studies with High-Dimensional Covariates

Causal inference in observational settings typically rests on a pair of ...
11/07/2017 ∙ by Alexander D'Amour, et al. ∙ 0 ∙ share

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Reducing Reparameterization Gradient Variance

Optimization with noisy gradients has become ubiquitous in statistics an...
05/22/2017 ∙ by Andrew C. Miller, et al. ∙ 0 ∙ share

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