Generalized Independence Noise (GIN) condition-based method
Algorithm Introduction
Learning the structure of Linear, Non-Gaussian LAtent variable Model (LiNLAM) based the GIN 1 condition.
Usage
from causal-learn.search.FCMBased.GIN.GIN import GIN
G, K = GIN(data)
Parameters
data: numpy ndarray. Data set.
Returns
G: GeneralGraph. Causal graph.
K: list. Causal Order.
- 1
Xie, F., Cai, R., Huang, B., Glymour, C., Hao, Z., & Zhang, K. (2020, January). Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. In NeurIPS.