US 11,947,552 B2
Method for discovering causality from data, electronic device and storage medium
Xu Li, Beijing (CN); Yunfeng Cai, Beijing (CN); Mingming Sun, Beijing (CN); and Ping Li, Sunnyvale, CA (US)
Assigned to BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing (CN)
Filed by BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing (CN)
Filed on Sep. 19, 2022, as Appl. No. 17/947,659.
Claims priority of application No. 202210177580.6 (CN), filed on Feb. 25, 2022.
Prior Publication US 2023/0273932 A1, Aug. 31, 2023
Int. Cl. G06F 17/00 (2019.01); G06F 16/22 (2019.01); G06F 16/2458 (2019.01)
CPC G06F 16/2465 (2019.01) [G06F 16/2237 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for discovering causality from data, comprising:
acquiring to-be-processed data which is body data corresponding to different users, and obtaining a covariance matrix of the to-be-processed data;
determining a first target column in the covariance matrix, taking the number of columns of the first target column as a first place in a rearrangement sequence, and obtaining a first upper triangular matrix according to the first target column;
determining a position of the number of columns of the covariance matrix other than the first target column except the first place in the rearrangement sequence according to the first target column and the first upper triangular matrix, and obtaining an upper triangular matrix in each position determination;
obtaining an adjacency matrix according to an upper triangular matrix and a rearrangement sequence obtained in final position determination; and
generating a directed acyclic graph (DAG) by using the adjacency matrix, and taking the DAG as a causality discovery result of the to-be-processed data.