TimeLovercc/Awesome-Graph-Causal-Learning
A list of Graph Causal Learning materials.
This resource curates research papers and materials focused on understanding cause-and-effect relationships within complex network data. It helps researchers, PhD students, and data scientists by providing a structured collection of papers on topics like improving model fairness, explaining predictions, and generalizing models to new data. The primary input is a need for academic knowledge on Graph Causal Learning, and the output is a categorized list of relevant research papers, often with links to PDFs and code.
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Use this if you are a researcher or practitioner in machine learning or data science who needs to understand or apply causal inference techniques to data represented as graphs.
Not ideal if you are looking for an immediate, ready-to-use software library or a step-by-step tutorial for implementing causal graph models without prior research background.
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Jan 24, 2025
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