47 citations · 116 across the 35 of their papers we have counts for
35 papers
Conditional Local Independence Testing for Itô processes with Applications to Dynamic Causal Discovery
Mingzhou Liu, Xinwei Sun, Yizhou Wang
Inferring causal relationships from dynamical systems is the central interest of many scientific inquiries. Conditional local independence, which describes whether the evolution of…
Bayesian Intervention Optimization for Causal Discovery
Yuxuan Wang, Mingzhou Liu, Xinwei Sun +2
Causal discovery is crucial for understanding complex systems and informing decisions. While observational data can uncover causal relationships under certain assumptions, it often…
The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
Yong Wu, Mingzhou Liu, Jing Yan +4
Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the con…
Learning Causal Alignment for Reliable Disease Diagnosis
Mingzhou Liu, Ching-Wen Lee, Xinwei Sun +2
Aligning the decision-making process of machine learning algorithms with that of experienced radiologists is crucial for reliable diagnosis. While existing methods have attempted t…
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
Wang Lu, Jindong Wang, Xinwei Sun +4
Time series remains one of the most challenging modalities in machine learning research. The out-of-distribution (OOD) detection and generalization on time series tend to suffer du…
Causal Discovery via Conditional Independence Testing with Proxy Variables
Mingzhou Liu, Xinwei Sun, Yu Qiao +1
Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias…