Structural Causal Analysis of Data Variation With Exogenous Noise
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Solution Overview
Problem
Existing methods for analyzing data variations fail to accurately identify both direct and indirect causal factors, as they primarily focus on statistical relationships without ensuring causal relationships, leading to incomplete understanding of data changes.
Innovation Solution
An information processing device employing a structural causal model to calculate exogenous noise estimation values and generate a degree of contribution, which identifies both direct and indirect causes of data variation by utilizing a hardware processor to analyze result data through a structural causal model, adjacency matrix, and exogenous noise estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If regression models are used to analyze data variations, then computational effort is reduced, but the ability to identify both direct and indirect causal factors is insufficient
Solution Approach 1:
The patent segments the causal analysis into two distinct components: direct effects (captured by regression coefficients) and indirect effects (captured by exogenous noise estimation). This segmentation allows the system to separately calculate and combine direct and indirect causal contributions, achieving comprehensive causal factor identification while maintaining computational efficiency through modular processing steps.
2Loss of information
If statistical relationships are analyzed without ensuring causal relationships, then analysis simplicity is maintained, but incomplete understanding of data changes occurs
Solution Approach 1:
The patent introduces exogenous noise as an intermediary component that captures indirect causal relationships. By estimating exogenous noise and calculating its contribution to target variables, the system mediates between direct regression effects and indirect causal pathways, thereby preserving complete causal understanding without requiring overly complex structural causal models.
3Measurement precision
If existing methods focus primarily on statistical relationships, then ease of operation is maintained, but accurate identification of causal factors is compromised
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically calculates exogenous noise estimation values and their contributions to target variables without requiring manual intervention. The hardware processor autonomously performs the causal analysis by processing result data through the established model, thereby maintaining ease of operation while achieving accurate causal factor identification.
Data Source
AI summary
An information processing device includes a processing unit including a hardware processor. The hardware processor calculates plural exogenous-noise-estimation values corresponding to plural variables for each of one or more pieces of result data including plural result values respectively corresponding to the plural variables based on the result data and a structural-causal-model representing a causal-relationship of the plural variables. Each of the exogenous-noise-estimation values represents an estimation value of influence by an exogenous-noise different from influences from the plural variables on corresponding variables among the plural variables. The hardware processor generates a contribution-degree representing an influence-magnitude by the exogenous-noise given to a source variable as one of two variables to a target variable that is another variable for each combination of the two variables in the plural variables for the result data based on the structural-causal model and the plural exogenous-noise-estimation values for each result data.