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Found 5 papers, 1 papers with code
From Predictive Algorithms to Automatic Generation of Anomalies
Facing a similar problem -- how to extract theoretical insights from their intuitions -- researchers often turned to ``anomalies:'' constructed examples that highlight flaws in an existing theory and spur the development of new ones.
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.
Characterizing Fairness Over the Set of Good Models Under Selective Labels
We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance, or "the set of good models."
Design-Based Uncertainty for Quasi-Experiments
An interesting feature of our framework is that conventional standard errors tend to become more conservative when treatment probabilities vary more across units, i. e. when there is more selection into treatment.
