Search Results for author: Alexander D'Amour

Found 16 papers, 7 papers with code

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

no code implementations NeurIPS 2021 Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein

We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn't change model predictions.

Causal Inference Text Classification

Counterfactual Invariance to Spurious Correlations in Text Classification

no code implementations NeurIPS 2021 Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein

We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn't change model predictions.

Causal Inference Text Classification

Causally motivated Shortcut Removal Using Auxiliary Labels

1 code implementation13 May 2021 Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, Alexander D'Amour

Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning.

Causal Inference Disentanglement +1

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

no code implementations12 Apr 2021 Alexander D'Amour, Alexander Franks

We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data.

Dimensionality Reduction

Revisiting Rashomon: A Comment on "The Two Cultures"

no code implementations5 Apr 2021 Alexander D'Amour

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

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

1 code implementation NeurIPS 2021 Steve Yadlowsky, Taedong Yun, Cory McLean, Alexander D'Amour

The key insight of SLOE is that the Sur and Cand\`es (2019) correction can be reparameterized in terms of the \emph{corrupted signal strength}, which is only a function of the estimated parameters $\widehat \beta$.

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

1 code implementation18 Feb 2021 Jiajing Zheng, Alexander D'Amour, Alexander Franks

Our method is based on a copula factorization of the joint distribution of outcomes, treatments, and confounders, and can be layered on top of arbitrary observed data models.

Causal Identification Causal Inference Methodology

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

no code implementations19 Jun 2020 Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek

Using this one line code change, we achieve state-of-the-art on recent covariate shift benchmarks and an mCE of 60. 28\% on the challenging ImageNet-C dataset; to our knowledge, this is the best result for any model that does not incorporate additional data augmentation or modification of the training pipeline.

Data Augmentation

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

1 code implementation11 Nov 2019 Niklas T. Rindtorff, MingYu Lu, Nisarg A. Patel, Huahua Zheng, Alexander D'Amour

Here, we propose a benchmark dataset to evaluate contextual bandit algorithms based on real in vitro drug response of approximately 900 cancer cell lines.

Decision Making

Comment: Reflections on the Deconfounder

no code implementations17 Oct 2019 Alexander D'Amour

The aim of this comment (set to appear in a formal discussion in JASA) is to draw out some conclusions from an extended back-and-forth I have had with Wang and Blei regarding the deconfounder method proposed in "The Blessings of Multiple Causes" [arXiv:1805. 06826].

Causal Identification

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