iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
iSCAN：识别非线性加性噪声模型中的因果机制变化
Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the underlying causal structure is often unknown, and estimating it from data remains a challenging task. In many situations, however, the end goal is to localize the changes (shifts) in the causal mechanisms between related datasets instead of learning the full causal structure of the individual datasets. Some…