Causal Structure Factor Contribution Quantification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack a suitable solution for determining specific contribution degrees of factors to a target effect in causal structures, which is essential for in-depth analysis and policy establishment in various application scenarios.
Innovation Solution
A method that utilizes a causal structure to automatically decompose the contribution degrees of factors to target observed data, allowing for the measurement of each factor's contribution to the observed data of a target factor, based on the causal structure and corresponding observed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If causal relationship analysis is performed to enable in-depth analysis and policy establishment, then the ability to establish sales policies and treatment plans is improved, but the complexity of data processing and causal structure determination increases
Solution Approach 1:
The patent segments the complex causal analysis process into distinct modules: (1) obtaining observed data from multiple factors, (2) determining causal relationships between factors, (3) calculating contribution degrees of individual factors to target outcomes, and (4) presenting results. This segmentation transforms an overwhelming complex task into manageable sequential steps, resolving the contradiction between analytical capability and processing complexity.
2Measurement precision
If contribution degree determination is implemented for each factor, then measurement precision of factor impact is improved, but the computational resources and processing time required increase
Solution Approach 1:
The patent performs preliminary actions by first establishing the causal structure and identifying relevant factors before conducting the actual contribution degree calculations. The system pre-processes the data by determining which factors have causal relationships with the target, filtering out irrelevant factors beforehand. This preliminary structuring reduces the computational burden during the actual measurement phase, achieving precise contribution degree determination while minimizing processing time.
3Loss of information
If comprehensive causal structure analysis is conducted to identify all factor relationships, then the completeness of analysis is improved, but the difficulty of detecting and measuring relationships increases
Solution Approach 1:
The patent replaces manual or simple statistical methods for detecting causal relationships with advanced machine learning algorithms. The system employs trained models that can automatically identify causal structures from observed data, substituting complex mechanical detection processes with intelligent computational systems. This enables comprehensive analysis of factor relationships while reducing the practical difficulty of detection and measurement.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, device and computer-readable storage medium for data processing. A method for data processing comprises: obtaining observed data corresponding to a plurality of factors to be analyzed; in response to one of the plurality of factors being selected as a target factor, obtaining a causal structure of the plurality of factors, the causal structure indicating causal relationships between the plurality of factors; and determining a contribution degree of a first factor of the plurality of factors to target observed data of the target factor based on the causal structure and the observed data corresponding to the plurality of factors. This solution can effectively quantify specific degrees of impact of the respective factors in the causal relationships to current observed data of the target factor, which is beneficial to analysis and policy establishment in various application scenarios.