284 citations · 587 across the 49 of their papers we have counts for
56 papers
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…
A Unified Framework for Semiparametrically Efficient Semi-Supervised Learning
Zichun Xu, Daniela Witten, Ali Shojaie
We consider statistical inference under a semi-supervised setting where we have access to both a labeled dataset consisting of pairs and an unlabeled datase…
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…
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm
Xiudi Li, Abolfazl Safikhani, Ali Shojaie
Regime shifts in high-dimensional time series arise naturally in many applications, from neuroimaging to finance. This problem has received considerable attention in low-dimensiona…
Inference for linear functionals of high-dimensional longitudinal proteomics data using generalized estimating equations
Lu Xia, Ali Shojaie
Regression analysis of correlated data, where multiple correlated responses are recorded on the same unit, is ubiquitous in many scientific areas. With the advent of new technologi…
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…
Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodes
Xu Wang, Ali Shojaie
Thanks to technological advances leading to near-continuous time observations, emerging multivariate point process data offer new opportunities for causal discovery. However, a key…
Direct estimation and inference of differential Granger causality between two high-dimensional time series
Yue Wang, Jing Ma, Ali Shojaie
Comparing dependence structures between two related multivariate time series is of fundamental interest in many scientific applications, where changes may occur in both directed te…
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…
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…
Nonparametric causal structure learning in high dimensions
Shubhadeep Chakraborty, Ali Shojaie
The PC and FCI algorithms are popular constraint-based methods for learning the structure of directed acyclic graphs (DAGs) in the absence and presence of latent and selection vari…
Definite Non-Ancestral Relations and Structure Learning
Wenyu Chen, Mathias Drton, Ali Shojaie
In causal graphical models based on directed acyclic graphs (DAGs), directed paths represent causal pathways between the corresponding variables. The variable at the beginning of s…
Inference on function-valued parameters using a restricted score test
Aaron Hudson, Marco Carone, Ali Shojaie
It is often of interest to make inference on an unknown function that is a local parameter of the data-generating mechanism, such as a density or regression function. Such estimand…
Granger Causality: A Review and Recent Advances
Ali Shojaie, Emily B. Fox
Introduced more than a half century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genom…
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure
Yunhua Xiang, Tianyu Zhang, Xu Wang +2
Originally developed for imputing missing entries in low rank, or approximately low rank matrices, matrix completion has proven widely effective in many problems where there is no…
Generalized Matrix Decomposition Regression: Estimation and Inference for Two-way Structured Data
Yue Wang, Ali Shojaie, Timothy W. Randolph +2
This paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition re…
Statistical Inference for Qualitative Interactions with Applications to Precision Medicine and Differential Network Analysis
Aaron Hudson, Ali Shojaie
Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub-population. Of particular interest in many biomedical settings are absence/pr…
Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks
Aaron Hudson, Ali Shojaie
Differences between biological networks corresponding to disease conditions can help delineate the underlying disease mechanisms. Existing methods for differential network analysis…
Generalized Score Matching for General Domains
Shiqing Yu, Mathias Drton, Ali Shojaie
Estimation of density functions supported on general domains arises when the data is naturally restricted to a proper subset of the real space. This problem is complicated by typic…
Statistical Inference for Networks of High-Dimensional Point Processes
Xu Wang, Mladen Kolar, Ali Shojaie
Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional poi…
Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks
Simge Kucukyavuz, Ali Shojaie, Hasan Manzour +2
Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form of a directed acyclic graph (DAG), and have found diverse ap…
Directed Graphical Models and Causal Discovery for Zero-Inflated Data
Shiqing Yu, Mathias Drton, Ali Shojaie
Modern RNA sequencing technologies provide gene expression measurements from single cells that promise refined insights on regulatory relationships among genes. Directed graphical…
Differential Network Analysis: A Statistical Perspective
Ali Shojaie
Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines. Growing evidence, especially from bio…
Statistical significance in high-dimensional linear mixed models
Lina Lin, Mathias Drton, Ali Shojaie
This paper concerns the development of an inferential framework for high-dimensional linear mixed effect models. These are suitable models, for instance, when we have repeated…