29 citations · 73 across the 33 of their papers we have counts for
11 papers · 1 filter
Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Debo Cheng, Ziqi Xu, Jiuyong Li +4
Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the pres…
Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference
Debo Cheng, Yang Xie, Ziqi Xu +5
In causal inference, it is a fundamental task to estimate the causal effect from observational data. However, latent confounders pose major challenges in causal inference in observ…
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
Ziqi Xu, Debo Cheng, Jiuyong Li +3
An essential and challenging problem in causal inference is causal effect estimation from observational data. The problem becomes more difficult with the presence of unobserved con…
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference
Debo Cheng, Ziqi Xu, Jiuyong Li +3
This paper studies the challenging problem of estimating causal effects from observational data, in the presence of unobserved confounders. The two-stage least square (TSLS) method…
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 Intervention for Measuring Confidence in Drug-Target Interaction Prediction
Wenting Ye, Chen Li, Yang Xie +5
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development. They play a crucial role in assisting scientists in finding new drugs…
Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
Chen Li, Yang Cao, Ye Zhu +3
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy.…
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
Ziqi Xu, Debo Cheng, Jiuyong Li +3
An essential problem in causal inference is estimating causal effects from observational data. The problem becomes more challenging with the presence of unobserved confounders. Whe…
FASTAGEDS: Fast Approximate Graph Entity Dependency Discovery
Guangtong Zhou, Selasi Kwashie, Yidi Zhang +5
This paper studies the discovery of approximate rules in property graphs. We propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) at almos…
Disentangled Representation for Causal Mediation Analysis
Ziqi Xu, Debo Cheng, Jiuyong Li +3
Estimating direct and indirect causal effects from observational data is crucial to understanding the causal mechanisms and predicting the behaviour under different interventions.…
Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation
Haoran Zhao, Yinghao Zhang, Debo Cheng +2
Causal inference plays an important role in under standing the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the ave…