Causal Analysis System for Automated Factor Discovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional causal analysis methods require manual interactions, leading to low efficiency and an inability to effectively discover causal relationships among multiple factors, which limits their application in fields like marketing research, manufacturing, and healthcare.
Innovation Solution
A computer-implemented system that automatically determines causal structures from observation samples, allows user input for optimization, identifies key factors affecting a target factor, and evaluates the effect of strategies to achieve desirable outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual causal analysis methods are used, then user control and interpretability are maintained, but efficiency and productivity are low
Solution Approach 1:
The system enables self-service by automatically discovering causal structures from observation samples without requiring manual intervention. The causal structure discovery module autonomously analyzes data, identifies causal relationships, and presents results to users, eliminating the need for manual causal analysis while maintaining user control over the process.
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated computational system. The causal structure discovery module uses algorithms to process observation samples and identify causal relationships, substituting human manual analysis with automated mechanical processing to significantly improve efficiency and productivity.
2Adaptability or versatility
If conventional manual methods are used, then simplicity of the system is maintained, but the ability to discover causal relationships among multiple factors is limited
Solution Approach 1:
The system segments the causal analysis process into distinct functional modules: data collection module, causal structure discovery module, and causal analysis module. Each module handles specific tasks independently, allowing the system to manage complexity through modular organization while enhancing its ability to discover causal relationships among multiple factors.
Solution Approach 2:
The causal structure discovery module serves multiple functions: it processes observation samples, identifies causal relationships, determines causal structures, and presents results. This multi-functionality enables the system to handle complex causal analysis tasks across different domains without requiring separate specialized tools for each function.
3Productivity
If automated causal analysis is implemented, then efficiency is improved, but user input and optimization opportunities are reduced
Solution Approach 1:
The system incorporates feedback mechanisms where users can review the discovered causal structures and provide input for optimization. The causal analysis module presents results to users who can then provide feedback to refine the analysis, maintaining ease of operation while preserving high efficiency through automated processing.
Data Source
AI summary
Embodiments of the present disclosure relate to methods, systems and computer program products for causal analysis. In some embodiments, there is provided a computer-implemented method. The method comprises determining, from observation samples of a plurality of factors, a first causal structure indicating a first causal relationship among the plurality of factors, each observation sample including a set of observation values of the plurality of factors; presenting the first causal structure to a user; in response to receiving at least one user input about the first causal structure from the user, executing actions associated with the at least one user input based on the first causal structure; and presenting a result of the execution of the actions to the user. In other embodiments, another method, systems and computer program products are provided.