2 citations · 5 across the 8 of their papers we have counts for
9 papers
Local Causal Discovery for Structural Evidence of Direct Discrimination
Jacqueline Maasch, Kyra Gan, Violet Chen +3
Identifying the causal pathways of unfairness is a critical objective for improving policy design and algorithmic decision-making. Prior work in causal fairness analysis often requ…
Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models
Sujai Hiremath, Jacqueline R. M. A. Maasch, Mengxiao Gao +2
Learning the unique directed acyclic graph corresponding to an unknown causal model is a challenging task. Methods based on functional causal models can identify a unique graph, bu…
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams
Brian Cho, Kyra Gan, Nathan Kallus
We propose a novel nonparametric sequential test for composite hypotheses for means of multiple data streams. Our proposed method, \emph{peeking with expectation-based averaged cap…
Online Uniform Sampling: Randomized Learning-Augmented Approximation Algorithms with Application to Digital Health
Xueqing Liu, Kyra Gan, Esmaeil Keyvanshokooh +1
Motivated by applications in digital health, this work studies the novel problem of online uniform sampling (OUS), where the goal is to distribute a sampling budget uniformly acros…
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
Jacqueline Maasch, Weishen Pan, Shantanu Gupta +3
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. Howeve…
Anytime-valid inference in N-of-1 trials
Ivana Malenica, Yongyi Guo, Kyra Gan +1
App-based N-of-1 trials offer a scalable experimental design for assessing the effects of health interventions at an individual level. Their practical success depends on the strong…
Contextual Bandits with Budgeted Information Reveal
Kyra Gan, Esmaeil Keyvanshokooh, Xueqing Liu +1
Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often re…
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
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. The learner is given a set of a…
Causal Inference With Selectively Deconfounded Data
Kyra Gan, Andrew A. Li, Zachary C. Lipton +1
Given only data generated by a standard confounding graph with unobserved confounder, the Average Treatment Effect (ATE) is not identifiable. To estimate the ATE, a practitioner mu…