Causality Search Reliability Through Resampled Graph Selection
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Solution Overview
Problem
Conventional causality search programs face reliability issues and increased user burden due to unsuitable loop formations and the need to sort through multiple causal graphs, especially with larger datasets.
Innovation Solution
A causality search system that generates multiple sample datasets from an original dataset, allowing for the determination of a single causal graph through switchable graph and directed edge selection modes, reducing user workload by displaying frequency of occurrence and probability of edges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple causal graphs are generated through sampling with replacement to improve reliability, then the reliability of causality search is improved, but the user burden in selecting directed edges increases
Solution Approach 1:
The system automatically determines the single causal graph by evaluating multiple candidate graphs against the original dataset without requiring manual user selection. The computer autonomously performs the selection process by comparing each candidate graph's predictions with the actual data, thereby reducing user burden while maintaining reliability through multiple sampling iterations
Solution Approach 2:
The system employs a feedback mechanism where each candidate causal graph is evaluated based on how well its predictions match the original dataset. The determination process uses this feedback to select the most reliable causal graph, ensuring that the final output is validated against the actual data while automating the selection process
2Reliability
If multiple sample datasets are generated and causality search is conducted on each, then the reliability of the causal graph is improved, but the complexity of determining the final causal graph increases
Solution Approach 1:
The determination process is segmented into distinct evaluation steps: generating multiple candidate causal graphs from sampled datasets, evaluating each candidate against the original dataset, and selecting the best match. This segmentation transforms a complex determination task into manageable, systematic steps that improve reliability without overwhelming complexity
Solution Approach 2:
The system generates multiple candidate causal graphs (excessive action) to ensure reliability, then applies a filtering and selection process to identify the single best graph. This approach uses partial evaluation of multiple options rather than requiring complete manual analysis of all possibilities, thereby managing complexity while maintaining thoroughness
Data Source
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AI summary
First, a plurality of sample datasets is generated from a single original dataset through sampling with replacement. Next, a plurality of causal graphs each showing a causal relationship between the variables using a directed edge is obtained by conducting causality search on each of the plurality of sample datasets. Then, a single causal graph is determined on the basis of the plurality of causal graphs. Thus, it is possible to improve the reliability of the causal graph. Furthermore, a graph selection mode and a directed edge selection mode are switchable to each other in determining the single causal graph. Thus, it is possible to reduce a burden on a user in the work of determining the causal graph.