4 citations · 7 across the 7 of their papers we have counts for
8 papers
Proxy-Adjusted Causal Discovery from Targeted Interventions
Li Chen, Wei Pan, Xiaotong Shen
Randomized perturbations can reveal downstream responses without identifying which causal relationships are direct. Conditioning on intermediate responses may induce associations t…
Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference
Li Chen, Xiaotong Shen, Wei Pan
Standard diffusion models are flexible estimators of complex distributions, but they do not encode causal structures and therefore do not by themselves support causal analysis. We…
Diffusion-Driven High-Dimensional Variable Selection
Minjie Wang, Xiaotong Shen, Wei Pan
Variable selection for high-dimensional, highly correlated data has long been a challenging problem, often yielding unstable and unreliable models. We propose a resample-aggregate…
Enhancing Causal Effect Estimation with Diffusion-Generated Data
Li Chen, Xiaotong Shen, Wei Pan
Estimating causal effects from observational data is inherently challenging due to the lack of observable counterfactual outcomes and even the presence of unmeasured confounding. T…
Causal Discovery with Generalized Linear Models through Peeling Algorithms
Minjie Wang, Xiaotong Shen, Wei Pan
This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous…
Discovery and inference of a causal network with hidden confounding
Li Chen, Chunlin Li, Xiaotong Shen +1
This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-bas…
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation
Ben Dai, Xiaotong Shen, Lin Yee Chen +2
In explainable artificial intelligence, discriminative feature localization is critical to reveal a blackbox model's decision-making process from raw data to prediction. In this ar…
Two-level monotonic multistage recommender systems
Ben Dai, Xiaotong Shen, Wei Pan
A recommender system learns to predict the user-specific preference or intention over many items simultaneously for all users, making personalized recommendations based on a relati…