Domain-Specific Causal Relation Inference via Algebraic Homomorphism
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Solution Overview
Problem
Determining causal relations between events becomes increasingly resource-intensive and labor-intensive as the amount of data collected grows, making it challenging to accurately detect relationships between causes and effects manually.
Innovation Solution
A computer-implemented method that receives a set of events and causality/entailment pairs, formulates them into domain-specific algebraic structures, and determines causal relations by mapping these structures using homomorphism and causal reasoning, enabling automated generation of causal relationships without manual analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to detect causal relationships, then accuracy can be maintained, but resource consumption and time requirements increase significantly as data volume grows
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-implemented system that uses algebraic structures and homomorphism mapping to detect causal relationships. The system formulates events as algebraic structures and automatically determines causal relations through computational algorithms, eliminating the need for manual analysis while maintaining accuracy.
Solution Approach 2:
The patent transforms events into algebraic structures with specific parameters (domains, predicates, arguments) and uses homomorphism mapping between these structures to determine causal relationships. This parameter transformation allows the system to process large volumes of data efficiently while preserving the logical relationships necessary for accurate causal detection.
2Measurement precision
If more data is collected to improve causal relationship detection, then detection accuracy may improve, but the complexity and resource requirements increase
Solution Approach 1:
The patent segments the complex task of causal relationship detection into distinct computational components: event formulation as algebraic structures, domain identification, predicate extraction, and homomorphism mapping. This segmentation allows the system to handle large datasets systematically through modular processing steps, reducing overall system complexity.
Solution Approach 2:
The patent introduces algebraic structures as an intermediary representation between raw events and causal relationships. By formulating events as algebraic structures with defined domains, predicates, and arguments, the system creates a standardized intermediate form that simplifies the causal reasoning process and reduces complexity.
3Productivity
If automated methods are used to process large amounts of data, then productivity increases, but measurement precision may deteriorate compared to manual analysis
Solution Approach 1:
The patent performs preliminary formulation of events as algebraic structures with explicitly defined domains, predicates, and arguments before causal relationship determination. This preliminary structuring ensures that all necessary information is organized and validated before the automated causal reasoning process, maintaining accuracy while enabling efficient processing.
Solution Approach 2:
The system uses homomorphism mapping between algebraic structures to provide a rigorous mathematical framework for causal reasoning. The mapping preserves structural relationships and allows the system to verify causal inferences through mathematical consistency checks, ensuring accuracy in automated processing.
Data Source
AI summary
A method of determining causal relations includes receiving a set of events, a selected event and a request to determine whether the set of events has a causal relation with the selected event, receiving a causality collection including a plurality of causality pairs, and receiving an entailment collection including a plurality of entailment pairs, each entailment pair including a first event and a second event. A first group of events is selected from the causality and entailment collections, and the first group of events is formulated as a first domain-specific algebraic structure. The method further includes selecting a second group of events from the causality and entailment collections, formulating the second group of events as a second domain-specific algebraic structure, and determining a causal relation between the set of events and the selected event based on a relation between the first structure and the second structure.