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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...
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03/03/2023
When does Privileged Information Explain Away Label Noise?
Leveraging privileged information (PI), or features available during tra...
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12/21/2022
Adapting to Latent Subgroup Shifts via Concepts and Proxies
We address the problem of unsupervised domain adaptation when the source...
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11/28/2022
Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations
Spurious correlations, or correlations that change across domains where ...
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09/20/2022
Fairness and robustness in anti-causal prediction
Robustness to distribution shift and fairness have independently emerged...
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08/13/2022
Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes
We propose an approach for assessing sensitivity to unobserved confoundi...
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05/21/2022
Bias-robust Integration of Observational and Experimental Estimators
We describe a simple approach for combining an unbiased and a (possibly)...
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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...
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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...
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06/30/2021
The MultiBERTs: BERT Reproductions for Robustness Analysis
Experiments with pretrained models such as BERT are often based on a sin...
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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...
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05/13/2021
Causally-motivated Shortcut Removal Using Auxiliary Labels
Robustness to certain distribution shifts is a key requirement in many M...
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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...
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04/05/2021
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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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...
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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...
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11/06/2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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07/16/2020
On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not g...
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06/19/2020
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
Covariate shift has been shown to sharply degrade both predictive accura...
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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...
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10/17/2019
Comment: Reflections on the Deconfounder
The aim of this comment (set to appear in a formal discussion in JASA) i...
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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...
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09/02/2018
Flexible sensitivity analysis for observational studies without observable implications
A fundamental challenge in observational causal inference is that assump...
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11/07/2017
Overlap in Observational Studies with High-Dimensional Covariates
Causal inference in observational settings typically rests on a pair of ...
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05/22/2017