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Causal Inference and Graph Structures
This chapter explores the complexities of causal inference and the impact of interventions on graph structures. It introduces a novel method for integrating unknown interventions into unsupervised training and discusses the challenges of identifying target nodes in networks. The conversation shifts focus to a supervised learning approach in causal discovery, assessing data biases and utilizing transformer architectures for improved predictions.