no code implementations • 23 May 2024 • Sujai Hiremath, Jacqueline R. M. A. Maasch, Mengxiao Gao, Promit Ghosal, Kyra Gan
Learning the unique directed acyclic graph corresponding to an unknown causal model is a challenging task.
no code implementations • 23 May 2024 • Jacqueline Maasch, Kyra Gan, Violet Chen, Agni Orfanoudaki, Nil-Jana Akpinar, Fei Wang
This limits the practicality of causal fairness analysis in complex or low-knowledge domains.
1 code implementation • 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
In the OUA problem, the algorithm is given a budget $b$ and a time horizon $T$, and an adversary then chooses a value $\tau^* \in [b, T]$, which is revealed to the algorithm online.
1 code implementation • 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, as 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
When 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.