Information processing device, information processing system, information processing method, and computer program product

Patent metadata

Field	Value
Publication number	US-2025384059-A1
Application number	US-202519053173-A
Country	US
Kind code	A1
Filing date	Feb 13, 2025
Priority date	Jun 18, 2024
Publication date	Dec 18, 2025
Grant date	—

Abstract

According to an embodiment, an information processing device includes one or more hardware processors configured to: generate a plurality of exogenous noise estimation values corresponding to a plurality of variables for each of one or more pieces of record data, based on a pre-update model being a structural causal model representing a causal relationship of the plurality of variables; determine whether a causal relationship of the plurality of variables represented by the one or more pieces of record data is different from the causal relationship represented by the pre-update model, based on independence between any two or more variables in the plurality of exogenous noise estimation values with respect to each of the one or more pieces of record data; and generate a post-update model being the structural causal model based on the one or more pieces of record data when determining that the causal relationships are different.

First claim

What is claimed is:

1 . An information processing device comprising one or more hardware processors configured to:
generate a plurality of exogenous noise estimation values corresponding to a plurality of variables for each of one or more pieces of record data including a plurality of record values respectively corresponding to the plurality of variables, based on the one or more pieces of record data and a pre-update model that is a structural causal model representing a causal relationship of the plurality of variables, the plurality of exogenous noise estimation values each representing estimation values of influence by exogenous noises that are different from influences from the plurality of variables with respect to corresponding variables among the plurality of variables; determine whether a causal relationship of the plurality of variables that is represented by the one or more pieces of record data is different from the causal relationship of the plurality of variables that is represented by the pre-update model, based on independence between any two or more variables in the plurality of exogenous noise estimation values with respect to each of the one or more pieces of record data; and generate a post-update model that is the structural causal model based on the one or more pieces of record data when determining that the causal relationships are different.

2 . The device according to claim 1 , wherein
the structural causal model is represented using an adjacency matrix representing a magnitude of influence from one variable to another variable for each combination of two variables in the plurality of variables, and the one or more hardware processors are configured to:
calculate an exogenous noise matrix by multiplying a record data matrix including the one or more pieces of record data by a matrix obtained by subtracting the adjacency matrix from an identity matrix, the exogenous noise matrix including the plurality of exogenous noise estimation values for each of the one or more pieces of record data; and
determine whether a causal relationship of the plurality of variables that is represented by the one or more pieces of record data is different from the causal relationship of the plurality of variables that is represented by the pre-update model, based on the exogenous noise matrix.

3 . The device according to claim 2 , wherein the one or more hardware processors are configured to:
calculate a measure of independence for each combination of two variables in the plurality of variables, the measure of independence representing independence between two or more columns or rows corresponding to the two variables in the exogenous noise matrix; compare the calculated measure of independence with a threshold for each combination of the two variables in the plurality of variables; and determine whether the causal relationship of the plurality of variables that is represented by the one or more pieces of record data is different from the causal relationship of the plurality of variables that is represented by the pre-update model, based on a comparison result between the measure of independence and the threshold for each combination of the two variables in the plurality of variables.

4 . The device according to claim 2 , wherein the one or more hardware processors are configured to:
calculate measure of independences for respective combinations of two variables in the plurality of variables, the measure of independence representing independence between two or more columns or rows corresponding to the two variables in the exogenous noise matrix; calculate a statistical value obtained by integrating the calculated measure of independences for the combinations of the two variables in the plurality of variables; and determine whether the causal relationship of the plurality of variables that is represented by the one or more pieces of record data is different from the causal relationship of the plurality of variables that is represented by the pre-update model, based on a comparison result between the statistical value and a threshold.

5 . The device according to claim 3 , wherein
the one or more hardware processors are configured to calculate, as the measure of independence, any value of a correlation coefficient, a rank correlation coefficient, a mutual information, a pairwise likelihood ratio, a distance correlation, a Hilbert-Schmidt independence criterion (HSIC), a maximal information coefficient (MIC), and a randomized dependence coefficient (RDC) between two columns corresponding to the two variables in the exogenous noise matrix, or a value obtained by combining two or more of them.

6 . The device according to claim 1 , wherein
the structural causal model is represented using an adjacency matrix representing influence from one variable to another variable for each combination of two variables in the plurality of variables, and the one or more hardware processors are configured to:
estimate a new value corresponding to a nonzero element in the adjacency matrix of the pre-update model using linear regression based on the one or more pieces of record data when determining that the causal relationships are different, and
generate the post-update model by updating a value of a nonzero element in the adjacency matrix of the pre-update model to the estimated new value.

7 . The device according to claim 1 , wherein
the structural causal model is represented using an adjacency matrix representing a magnitude of influence from one variable to another variable for each combination of two variables in the plurality of variables, and the one or more hardware processors are configured to generate the post-update model using regularized linear regression based on the one or more pieces of record data while causing a causal order to be the same as the adjacency matrix of the pre-update model, when determining that the causal relationships are different.

8 . The device according to claim 7 , wherein
the one or more hardware processors are configured to generate the post-update model using Lasso or Adaptive Lasso based on the one or more pieces of record data so as to cause the causal order of the post-update model to be the same as the causal order of the adjacency matrix of the pre-update model, when determining that the causal relationships are different.

9 . The device according to claim 7 , wherein
the one or more hardware processors are configured to generate the post-update model using any one of Adaptive Lasso, Transfer Lasso, or Adaptive Transfer Lasso with the pre-update model as an initial estimation amount, based on the one or more pieces of record data so as to cause the causal order to be the same as the adjacency matrix of the pre-update model, when determining that the causal relationships are different.

10 . The device according to claim 1 , wherein
the one or more hardware processors are configured to generate the post-update model including a causal order, based on the one or more pieces of record data when determining that the causal relationships are different.

11 . The device according to claim 10 , wherein
the one or more hardware processors are configured to generate the post-update model based on a causal discovery method based on non-Gaussianity.

12 . The device according to claim 11 , wherein
the one or more hardware processors are configured to generate the post-update model based on ICA-LiNGAM or DirectLiNGAM.

13 . The device according to claim 1 , wherein
the structural causal model is represented using an adjacency matrix representing a magnitude of influence from one variable t

Assignees

• Toshiba Kk

Inventors

• Takada Masaaki
• Shingaki Ryusei

Classifications

• G06F16/28 Primary

  Databases characterised by their database models, e.g. relational or object models · CPC title