Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning
学习可识别结构以避免深度神经网络监督因果学习的偏差
Causal discovery is a structured prediction task that aims to predict causal relations among variables based on their data samples. Supervised Causal Learning (SCL) is an emerging paradigm in this field. Existing Deep Neural Network (DNN)-based methods commonly adopt the "Node-Edge approach", in which the model first computes an embedding vector for each variable-node, then uses these variable-wise representations to concurrently and independently predict for each directed…