Structural Causal Model Updates for Changing Data Relationships
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Solution Overview
Problem
Existing methods for analyzing causal relationships in manufacturing and information systems fail to adapt to changes in causal structures over time, necessitating updates to models used for analysis.
Innovation Solution
An information processing device that generates a structural causal model by estimating external noise and determining changes in causal relationships using exogenous noise matrices, allowing for the update of models based on new data to reflect current system conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If causal discovery and causal inference are used to analyze causal relationships, then prediction accuracy and decision-making improvement are enhanced, but the model becomes outdated when causal structure changes over time
Solution Approach 1:
The patent implements a feedback mechanism where the causal model is continuously updated using recent record data. The determination unit repeatedly determines whether causal relationships have changed and updates the model when changes are detected, creating a closed-loop feedback system that adapts to changing causal structures while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms the static causal model into a dynamic one by introducing continuous updates based on changing causal structures. The model transitions from a fixed structure to a dynamically adapting structure that responds to new data, enabling it to maintain accuracy in non-stationary environments.
2Adaptability or versatility
If the model is continuously updated to reflect changing causal relationships, then adaptability to current system conditions is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by updating only the necessary portions of the causal model rather than completely relearning from scratch. The determination unit selectively updates causal relationships based on detected changes, and the learning unit performs targeted learning only where needed, reducing computational complexity compared to full model retraining.
Solution Approach 2:
The patent implements preliminary action by pre-establishing the causal structure through initial causal discovery, and then using this pre-built framework for subsequent updates. The pre-update model serves as a foundation that guides the update process, reducing the computational burden of adapting to changes compared to building models from scratch each time.
3Measurement precision
If causal relationships are determined based on independence of exogenous noise estimation values, then detection accuracy of causal changes is improved, but the complexity of noise estimation and independence testing increases
Solution Approach 1:
The patent introduces exogenous noise estimation values as an intermediary to bridge the gap between observed data and causal structure determination. By estimating these noise values and using their independence as a criterion, the system simplifies the complex task of detecting causal changes into a more manageable statistical testing problem, improving detection accuracy while keeping the process tractable.
Data Source
AI summary
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.