16 citations · 21 across the 9 of their papers we have counts for
9 papers
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…
A transformer boosted UNet for smoke segmentation in complex backgrounds in multispectral LandSat imagery
Jixue Liu, Jiuyong Li, Stefan Peters +1
Many studies have been done to detect smokes from satellite imagery. However, these prior methods are not still effective in detecting various smokes in complex backgrounds. Smokes…
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 Class-Specific Spectral Patterns to Improve Deep Learning Based Scene-Level Fire Smoke Detection from Multi-Spectral Satellite Imagery
Liang Zhao, Jixue Liu, Stefan Peters +3
Detecting fire smoke is crucial for the timely identification of early wildfires using satellite imagery. However, the spatial and spectral similarity of fire smoke to other confou…
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…