144 citations · 424 across the 30 of their papers we have counts for
8 papers · 1 filter
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 unified survey of treatment effect heterogeneity modeling and uplift modeling
Weijia Zhang, Jiuyong Li, Lin Liu
A central question in many fields of scientific research is to determine how an outcome would be affected by an action, or to measure the effect of an action (a.k.a treatment effec…
Local search for efficient causal effect estimation
Debo Cheng, Jiuyong Li, Lin Liu +3
Causal effect estimation from observational data is a challenging problem, especially with high dimensional data and in the presence of unobserved variables. The available data-dri…
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…