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20232026
most citedExploring Causal Learning through Graph Neural Networks: An In-depth Review

4 citations · 6 across the 4 of their papers we have counts for

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2 papers · 1 filter

cs.LG2026

Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders

Simi Job, Xiaohui Tao, Taotao Cai +3

Graph data is becoming increasingly prevalent due to the growing demand for relational insights in AI across various domains. Organizations regularly use graph data to solve comple…

cs.LG2026

Causal Neighbourhood Learning for Invariant Graph Representations

Simi Job, Xiaohui Tao, Taotao Cai +2

Graph data often contain noisy and spurious correlations that mask the true causal relationships, which are essential for enabling graph models to make predictions based on the und…