5 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…