Confounding is a major concern in epidemiology. Despite its significance, the different notions of confounding have not been fully appreciated in the literature, leading to confusion of causal concepts in epidemiology. In this article, we aim to highlight the importance of differentiating between the subtly different notions of confounding from the perspective of counterfactual reasoning. By us...

In this paper, we discuss several concepts in causal inference in terms of causal diagrams proposed by Pearl (1993, 1995a, b), and we give conditions for non-confounding, homogeneity and collapsibility for causal e€ects without knowledge of a completely constructed causal diagram. We ®rst introduce the concepts of non-confounding, conditional non-confounding, uniform non-confounding, homogeneit...

The prevailing graph neural network models have achieved significant progress in representation learning. However, this paper, we uncover an ever-overlooked phenomenon: the pre-trained learning model tested with full graphs underperforms well-pruned graphs. This observation reveals that there exist confounders graphs, which may interfere semantic information, and current methods not eliminated ...