Sergey discusses the balance between training effort and understanding causality in AI models. He explains how pre-trained candidate graphs can significantly reduce the number of interventions needed to identify the right model structure. Despite the theoretical complexity, practical insights often allow for more efficient training, revealing that certain variables can be excluded based on their impact on model performance.
