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Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
We propose a unified methodology for the robust design and evaluation of predictive algorithms in selectively observed data under such unobserved confounding.
FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes
Our framework can simultaneously target multiple observable or counterfactual fairness criteria, and it enables users to combine a large number of previously trained and newly trained predictors.
Modeling Treatment Effect Modification in Multidrug-Resistant Tuberculosis in an Individual Patient Data Meta-Analysis
We propose a marginal structural model (MSM) for effect modification by different patient characteristics and co-medications in a meta-analysis of observational IPD.
Methodology
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