Decision Tree Updating for Accurate Causal Graph Generation
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Solution Overview
Problem
Conventional causal discovery techniques struggle to determine appropriate conditions for classifying data and generating causal graphs, leading to decreased accuracy and increased workload, especially when users lack prior knowledge.
Innovation Solution
An information processing method that iteratively generates and updates decision trees to identify proper conditions for generating causal graphs, using methods like LinGAM or No-tears, by comparing scores of generated causal graphs to improve accuracy and reduce manual workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional causal discovery techniques are used to generate causal graphs, then the process can be automated, but the accuracy of causal graph generation decreases when users lack prior knowledge for setting appropriate conditions
Solution Approach 1:
The system automatically determines classification conditions and generates causal graphs without requiring user expertise. The causal discovery device autonomously performs data classification, causal relationship detection, and graph generation, enabling non-expert users to obtain accurate causal graphs through fully automated processing.
Solution Approach 2:
The patent introduces intermediate processing steps including automatic data classification and causal relationship detection as mediators between raw data and final causal graphs. These intermediary processes handle the complexity of condition setting and causal inference, bridging the gap between automated processing and accurate results.
2Measurement precision
If manual condition setting is performed for causal graph generation, then accuracy may improve, but operator workload increases
Solution Approach 1:
The system eliminates the need for manual condition setting by implementing self-service automation. The causal discovery device automatically determines classification conditions, performs data analysis, and generates causal graphs, completely removing the burden of manual operation while maintaining high accuracy through sophisticated algorithms.
Solution Approach 2:
The patent replaces manual mechanical operations with automated computational processes. Instead of operators manually setting conditions and analyzing data, the system uses computer-based causal discovery algorithms, machine learning models, and automated reasoning engines to perform these tasks with higher precision and zero manual effort.
3Measurement precision
If data is classified under multiple conditions to generate different causal graphs, then accuracy for specific conditions improves, but the complexity of the process increases
Solution Approach 1:
The patent segments the data processing task into distinct automated stages: data classification, causal relationship detection, and graph generation. Each stage handles specific aspects of the analysis independently, allowing the system to manage complex multi-condition classification through modular, organized processing steps that reduce overall system complexity.
Solution Approach 2:
The system dynamically adjusts classification conditions and processing parameters based on the characteristics of the input data. The automated causal discovery process adapts its methodology and complexity level according to the specific requirements of each dataset, optimizing the balance between accuracy and process complexity for different scenarios.
Data Source
AI summary
A method for processing information for a first and second decision tree that are mutually different, the former representing, by a path from a root to a leaf, a condition for classifying multiple data from which a causal graph is generated, the latter being based on the former, the method being executed by a computer and including: generating a first causal graph, for each path from the root to a leaf of the first decision tree, based on data that among the multiple data, meets the condition; after generating the first causal graph, repeatedly performing until an end condition is satisfied: generating a second causal graph, for each path from the root to a leaf of the second decision tree, based on the data that meets the condition; and updating the first decision tree with the second decision tree when a second score evaluating a likelihood of the second causal graph is better than a first score evaluating a likelihood of the first causal graph.