Causal Graph Construction Using LLM Priors and Monte Carlo Search
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Solution Overview
Problem
Existing causal graph construction methods face challenges in accuracy and effectiveness due to reliance on data-driven approaches, leading to inaccurate causal relationships and poor performance in downstream tasks.
Innovation Solution
A method and apparatus that utilize a large language model to determine priori knowledge and a Monte Carlo tree search with data independence tests to construct a causal graph, ensuring accurate causal relationships for improved downstream task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If data-driven causal discovery methods (independence test-based or large language model-based) are used, then the construction process can be automated, but the accuracy of causal relationships deteriorates
Solution Approach 1:
The patent merges two previously separate approaches: data-driven independence testing and knowledge-driven large language model reasoning. The system combines LLM-generated causal hypotheses with statistical independence tests to validate relationships, creating a hybrid method that leverages both automated processing and high accuracy.
Solution Approach 2:
The patent introduces an intermediary verification mechanism where LLM-generated causal relationships are not directly accepted but are instead subjected to independence testing as an intermediate validation step. This intermediary process filters out inaccurate causal claims while preserving automated processing.
2Extent of automation
If data-driven independence testing is used to determine causal relationships, then automation is achieved, but the accuracy of causal graphs deteriorates
Solution Approach 1:
The patent applies preliminary action by using the LLM to generate causal hypotheses before conducting independence tests. This pre-processing step narrows down the search space and guides the subsequent statistical testing, making the automated process more accurate.
Solution Approach 2:
The system implements feedback by using independence test results to validate and refine LLM-generated causal relationships. The feedback loop allows the system to correct inaccurate causal inferences while maintaining automated processing.
3Ease of operation
If large language model knowledge is used alone to determine causal relationships, then the process is simplified, but the accuracy and effectiveness for downstream tasks deteriorates
Solution Approach 1:
The patent introduces independence testing as an intermediary validation layer between the LLM and the final causal graph. This maintains the simplicity of using LLM while adding a layer of statistical verification to improve accuracy.
Data Source
AI summary
The present application provides a task variable causal graph construction method and apparatus, a device, and a medium. The method includes: obtaining a variable set and application task information corresponding to a target application task; determining priori knowledge by using a large language model based on the variable set and the application task information corresponding to the target application task; and determining a best causal graph by using a Monte Carlo tree search method based on the priori knowledge and data independence tests, where nodes in the best causal graph correspond one-to-one to the variables in the variable set, each edge in the best causal graph represents a causal relationship between variables corresponding to two nodes connected by the edge, and the best causal graph is used for root cause analysis of a downstream task. The present application improves the accuracy of causal graphs and the effectiveness of downstream tasks.