1 citations · 2 across the 6 of their papers we have counts for
6 papers
TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
Wentao Gao, Ziqi Xu, Jiuyong Li +6
As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting be…
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
Yang Xie, Ziqi Xu, Debo Cheng +4
Estimating causal effects from observational data is challenging, especially in the presence of latent confounders. Much work has been done on addressing this challenge, but most o…
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…
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…
Linking a predictive model to causal effect estimation
Jiuyong Li, Lin Liu, Ziqi Xu +3
A predictive model makes outcome predictions based on some given features, i.e., it estimates the conditional probability of the outcome given a feature vector. In general, a predi…