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Found 6 papers, 1 papers with code
Deconfounding Time Series Forecasting
Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making.
TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
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 becomes increasingly crucial across various domains.
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
As network data applications continue to expand, causal inference within networks has garnered increasing attention.
A Deconfounding Approach to Climate Model Bias Correction
This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders.
Estimating Peer Direct and Indirect Effects in Observational Network Data
Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions.
Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders.
