
The MultiBERTs: BERT Reproductions for Robustness Analysis
Experiments with pretrained models such as BERT are often based on a sin...
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Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests
Informally, a `spurious correlation' is the dependence of a model on som...
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Causallymotivated Shortcut Removal Using Auxiliary Labels
Robustness to certain distribution shifts is a key requirement in many M...
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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...
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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...
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SLOE: A Faster Method for Statistical Inference in HighDimensional Logistic Regression
Logistic regression remains one of the most widely used tools in applied...
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Copulabased Sensitivity Analysis for MultiTreatment Causal Inference with Unobserved Confounding
Recent work has focused on the potential and pitfalls of causal identifi...
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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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On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not g...
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Evaluating PredictionTime Batch Normalization for Robustness under Covariate Shift
Covariate shift has been shown to sharply degrade both predictive accura...
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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...
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Comment: Reflections on the Deconfounder
The aim of this comment (set to appear in a formal discussion in JASA) i...
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On MultiCause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives
Unobserved confounding is a central barrier to drawing causal inferences...
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Flexible sensitivity analysis for observational studies without observable implications
A fundamental challenge in observational causal inference is that assump...
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Overlap in Observational Studies with HighDimensional Covariates
Causal inference in observational settings typically rests on a pair of ...
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Reducing Reparameterization Gradient Variance
Optimization with noisy gradients has become ubiquitous in statistics an...
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Alexander D'Amour
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