no code implementations • 9 Feb 2024 • Brian Cho, Kyra Gan, Nathan Kallus
We propose a novel nonparametric sequential test for composite hypotheses for means of multiple data streams.
no code implementations • 3 Feb 2024 • Xueqing Liu, Kyra Gan, Esmaeil Keyvanshokooh, Susan Murphy
We propose two online approximation algorithms for this problem, one with and one without learning augmentation, and provide rigorous theoretical performance guarantees for them using competitive ratio analysis.
no code implementations • 25 Oct 2023 • Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang
Causal discovery is crucial for causal inference in observational studies: it can enable the identification of valid adjustment sets (VAS) for unbiased effect estimation.
no code implementations • 14 Jun 2023 • Brian Cho, Yaroslav Mukhin, Kyra Gan, Ivana Malenica
In the problem of estimating target parameters in nonparametric models with nuisance parameters, substituting the unknown nuisances with nonparametric estimators can introduce "plug-in bias."
no code implementations • 29 May 2023 • Kyra Gan, Esmaeil Keyvanshokooh, Xueqing Liu, Susan Murphy
Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments.
no code implementations • NeurIPS 2021 • Kyra Gan, Su Jia, Andrew Li
In the problem of active sequential hypothesis testing (ASHT), a learner seeks to identify the true hypothesis from among a known set of hypotheses.
no code implementations • 25 Feb 2020 • Kyra Gan, Andrew A. Li, Zachary C. Lipton, Sridhar Tayur
In this paper, we consider the benefit of incorporating a large confounded observational dataset (confounder unobserved) alongside a small deconfounded observational dataset (confounder revealed) when estimating the ATE.