35 citations · 65 across the 5 of their papers we have counts for
8 papers · 1 filter
Learning Conditional Instrumental Variable Representation for Causal Effect Estimation
Debo Cheng, Ziqi Xu, Jiuyong Li +3
One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data. However, causal effect est…
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…
Any Part of Bayesian Network Structure Learning
Zhaolong Ling, Kui Yu, Hao Wang +2
We study an interesting and challenging problem, learning any part of a Bayesian network (BN) structure. In this challenge, it will be computationally inefficient using existing gl…
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…
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…
Treatment effect estimation with disentangled latent factors
Weijia Zhang, Lin Liu, Jiuyong Li
Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confoun…