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20132026
most citedGranger Causality Networks for Categorical Time Series

11 citations · 42 across the 18 of their papers we have counts for

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20 papers · 1 filter

stat.ME2026

Expert-Guided g-computation with Large Language Models for Estimating Causal Effects on Timings: Applications to Hospital Quality Improvement

Patrick Vossler, Jialin Ouyang, F. Richard Guo +5

Hospital quality improvement (QI) programs routinely face multiple candidate interventions to optimize hospital flow, but existing methods struggle to estimate and rank the causal…

stat.ME2023

Semi-Parametric Inference for Doubly Stochastic Spatial Point Processes: An Approximate Penalized Poisson Likelihood Approach

Si Cheng, Jon Wakefield, Ali Shojaie

Doubly-stochastic point processes model the occurrence of events over a spatial domain as an inhomogeneous Poisson process conditioned on the realization of a random intensity func…

stat.ME20212 cited

Joint Estimation and Inference for Multi-Experiment Networks of High-Dimensional Point Processes

Xu Wang, Ali Shojaie

Modern high-dimensional point process data, especially those from neuroscience experiments, often involve observations from multiple conditions and/or experiments. Networks of inte…

stat.ME20211 cited

Direct estimation of differential Granger causality between two high-dimensional time series

Yue Wang, Jing Ma, Ali Shojaie

Differential Granger causality, that is understanding how Granger causal relations differ between two related time series, is of interest in many scientific applications. Modeling…

stat.ME2021

Interaction Models and Generalized Score Matching for Compositional Data

Shiqing Yu, Mathias Drton, Ali Shojaie

Applications such as the analysis of microbiome data have led to renewed interest in statistical methods for compositional data, i.e., multivariate data in the form of probability…

stat.ME20212 cited

Causal Structural Learning Via Local Graphs

Wenyu Chen, Mathias Drton, Ali Shojaie

We consider the problem of learning causal structures in sparse high-dimensional settings that may be subject to the presence of (potentially many) unmeasured confounders, as well…