28 citations · 34 across the 4 of their papers we have counts for
16 papers
Causal Inference with Conditional Instruments using Deep Generative Models
Debo Cheng, Ziqi Xu, Jiuyong Li +3
The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders…
Learning causal representations for robust domain adaptation
Shuai Yang, Kui Yu, Fuyuan Cao +3
Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all exist…
Sufficient Dimension Reduction for Average Causal Effect Estimation
Debo Cheng, Jiuyong Li, Lin Liu +1
Having a large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covaria…
Computational methods for cancer driver discovery: A survey
Vu Viet Hoang Pham, Lin Liu, Cameron Bracken +3
Motivation: Uncovering the genomic causes of cancer, known as cancer driver genes, is a fundamental task in biomedical research. Cancer driver genes drive the development and progr…
A general framework for causal classification
Jiuyong Li, Weijia Zhang, Lin Liu +3
In many applications, there is a need to predict the effect of an intervention on different individuals from data. For example, which customers are persuadable by a product promoti…
Towards unique and unbiased causal effect estimation from data with hidden variables
Debo Cheng, Jiuyong Li, Lin Liu +3
Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available.…