Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
用于估计含潜变量因果结构的广义独立噪声条件
We investigate the task of learning causal structure in the presence of latent variables, including locating latent variables and determining their quantity, and identifying causal relationships among both latent and observed variables. To this end, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured…