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Found 6 papers, 0 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.
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.
Physical System for Non Time Sequence Data
We propose a novelty approach to connect machine learning to causal structure learning by jacobian matrix of neural network w. r. t.
Gradient-based Causal Structure Learning with Normalizing Flow
In this paper, we propose a score-based normalizing flow method called DAG-NF to learn dependencies of input observation data.
