Causal Relationship Identification in Hierarchical Data Systems
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Solution Overview
Problem
Current data structures, such as relational databases and multidimensional data warehouses, fail to accurately capture significant causal relationships between nodes, especially those not immediately apparent in parent-child relationships, leading to stale data and computational inefficiencies in identifying actionable insights from large datasets.
Innovation Solution
A method that accesses a hierarchy of nodes in a data structure, identifies subsets with potential causal relationships, generates models to determine coefficients of influence, and ranks nodes affecting each model, incorporating user patterns, denormalization, and exogenous variables to improve computational efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive causal relationships between all nodes are analyzed, then measurement precision of causal relationships is improved, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the data structure into hierarchical levels (e.g., organizational units, departments, teams, individuals) and processes causal relationships at each level separately. This segmentation allows the system to analyze causal relationships in manageable chunks rather than attempting to process all node relationships simultaneously, thereby reducing computational complexity while maintaining detection accuracy through systematic level-by-level analysis.
Solution Approach 2:
The patent introduces temporal dimension by analyzing causal relationships across different time periods and hierarchical levels. By examining how relationships evolve over time and across organizational hierarchies, the system can identify significant causal patterns without needing to process all possible node relationships at every time point, thus reducing computational burden while improving measurement precision through multi-dimensional analysis.
2Reliability
If all nodes in the data structure are processed to identify causal relationships, then reliability of insights is improved, but loss of time and computational resources increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific hierarchical levels and node subsets that are most relevant to the analysis objectives. Instead of uniformly processing all nodes, the system identifies and prioritizes local regions of the data structure where causal relationships are most likely to occur or have greatest impact, thereby maintaining insight reliability while reducing overall processing time through targeted analysis.
Solution Approach 2:
The patent employs partial action by processing a carefully selected subset of nodes and relationships rather than exhaustively analyzing all possible combinations. The system identifies key influencer nodes and focuses computational efforts on analyzing relationships involving these nodes, achieving sufficient reliability for decision-making without the excessive time cost of complete graph analysis.
3Loss of information
If the search space for causal relationships is not narrowed, then completeness of relationship identification is improved, but productivity of the system decreases
Solution Approach 1:
The patent applies preliminary action by pre-processing the data structure to identify and flag potential causal relationships before conducting full analysis. The system performs initial screenings to detect nodes with high potential for causal influence based on data patterns, temporal correlations, or domain-specific criteria, thereby narrowing the search space in advance and maintaining relationship identification completeness while improving system productivity through pre-filtering.
Solution Approach 2:
The patent extracts and isolates the most significant causal relationships from the broader data structure for focused analysis. By separating high-priority relationships from the general dataset and analyzing them independently, the system maintains completeness of important relationship identification while improving productivity through efficient resource allocation to the most valuable insights.
Data Source
AI summary
A method of identifying causal relationships between time series may include accessing a hierarchy of nodes in a data structure, where each node in the plurality of nodes may include a time series of data. The method may also include identifying a subset of nodes in the plurality of nodes for which causal relationships may exist in the corresponding time series. The method may additionally include generating a model for each of the subset of nodes, where the model may receive the subset of nodes and generate coefficients indicating how strongly each of the subset of nodes causally affects other nodes in the subset of nodes. The method may further include generating a ranked output of nodes that causally affect a first node in the subset of nodes based on an output of the corresponding model.