Causal Graph Modeling With Collinearity Constraints
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Solution Overview
Problem
Existing substrate processing technologies lack the ability to derive causal structures between observable variables effectively, leading to potential errors in recognizing relationships and understanding process dynamics.
Innovation Solution
A non-transitory computer-readable recording medium and information processing apparatus that utilize a directed acyclic graph to model causal relationships between observable variables, incorporating edge pruning to prevent collinearity and allowing user interaction for modification, enabling robust causal structure derivation and prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If causal relationships are discovered based on observation data without constraint conditions, then the causal structure can be derived, but errors may occur due to collinearity between observable variables
Solution Approach 1:
The patent applies preliminary action by pre-defining constraint conditions that prohibit edges from being drawn from collinear observable variables before the causal structure derivation process begins. This prevents collinearity-induced errors from occurring in the first place, rather than correcting them after discovery. The constraint conditions are established a priori based on domain knowledge about the observation system.
Solution Approach 2:
The patent introduces constraint conditions as an intermediary mechanism that mediates between the observation data and the causal structure derivation process. These constraints act as a filter that prevents spurious causal relationships caused by collinearity, allowing the system to derive more reliable causal structures by blocking problematic inference paths.
2Adaptability or versatility
If the causal structure is derived using all observable variables, then comprehensive coverage is achieved, but the complexity of the directed acyclic graph increases
Solution Approach 1:
The patent applies the taking out principle by extracting and removing collinear observable variables from the causal structure derivation process through constraint conditions. This eliminates redundant or problematic variables that would otherwise increase graph complexity, while retaining the essential causal relationships among the remaining variables.
3Measurement precision
If constraint conditions are applied to prohibit edges from collinear variables, then causal accuracy is improved, but the flexibility of the causal structure derivation is reduced
Solution Approach 1:
The patent applies local quality by applying constraint conditions selectively only to collinear observable variables rather than imposing uniform restrictions on all variables. This localized approach maintains high flexibility for non-collinear variables while precisely targeting and correcting only the problematic collinear relationships, thus balancing accuracy improvement with derivation flexibility.
Data Source
AI summary
A non-transitory computer readable recording medium storing a computer program causing a computer to execute processing of acquiring observation data corresponding to a plurality of types of observable variables from an observation system to be monitored, discovering causal relationships between the observable variables based on the acquired observation data, modifying the causal relationships according to constraint conditions to be applied between the observable variables to derive a causal structure of the observable variables in the observation system, and generating a directed acyclic graph expressing the causal structure, using nodes indicating observable variables and edges indicating causal relationships between the nodes, wherein the constraint conditions include a condition that prohibits the edges from being drawn from a plurality of observable variables having collinearity to one observable variable.