Tag: causal inference
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L2C Framework: Unified Causal Discovery with Latent Variables
Discover the L2C framework that bridges local and cluster causal discovery, overcoming latent variable challenges for accurate macro-level inference.
CD-GNN: Causal Learning for Heterophilic Graphs
Discover how CD-GNN leverages causal disentanglement to improve accuracy in heterophilic graph learning by addressing spurious inductive subgraphs.
Forecast-Necessity Testing for Causal Discovery in Nonlinear Time-Series
Discover how forecast-necessity testing improves causal discovery and interpretation in nonlinear time-series models beyond traditional coefficients.
How Children and Adults Learn Causality via Empowerment
Explore how children and adults use empowerment, controllability, and variability to build causal models and enhance learning.
Causal Inference in Graph Representation Learning Explained
Explore how causal inference enhances graph representation learning by addressing aggregation challenges and improving model validity.