144 citations · 268 across the 38 of their papers we have counts for
7 papers · 1 filter
Disentangled Representation Learning for Causal Inference with Instruments
Debo Cheng, Jiuyong Li, Lin Liu +4
Latent confounders are a fundamental challenge for inferring causal effects from observational data. The instrumental variable (IV) approach is a practical way to address this chal…
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
Debo Cheng, Ziqi Xu, Jiuyong Li +4
Querying causal effects from time-series data is important across various fields, including healthcare, economics, climate science, and epidemiology. However, this task becomes com…
Linking Model Intervention to Causal Interpretation in Model Explanation
Debo Cheng, Ziqi Xu, Jiuyong Li +4
Intervention intuition is often used in model explanation where the intervention effect of a feature on the outcome is quantified by the difference of a model prediction when the f…
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…
Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction
Wentao Gao, Jiuyong Li, Debo Cheng +7
Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, the GCM Outputs exhibit systematic biases due to model unce…
From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification
Xin Liu, Weijia Zhang, Wei Tang +4
In whole slide images (WSIs) analysis, attention-based multi-instance learning (MIL) models are susceptible to spurious correlations and degrade under domain shift. These methods m…