Causal Chain Extraction via Neural Network Intervention Testing
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Solution Overview
Problem
Existing methods for causal discovery in complex systems, such as IT environments, struggle to accurately identify causal chains and root causes due to limitations in data availability and the inability to distinguish between causal and confounding events, often relying on statistical analyses of observational data and prone to mischaracterizing correlations as causative.
Innovation Solution
The described techniques exploit the representational power of neural networks through intervention testing, allowing for the identification of causal chains by modifying system inputs to determine causal effects across multiple timesteps, and using structural causal models to distinguish between causal and confounding events, enabling the extraction of accurate causal chains and root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If statistical analyses of observational data are used for causal discovery, then the method is simple and computationally efficient, but the accuracy of identifying causal chains deteriorates due to inability to distinguish causal and confounding events
Solution Approach 1:
The patent introduces structural causal models as an intermediary framework between observational data and causal conclusions. These models provide a formal structure that distinguishes causal relationships from confounding correlations, enabling accurate causal discovery while maintaining computational tractability through the structured approach.
Solution Approach 2:
The patent replaces traditional statistical analysis mechanisms with neural network-based intervention testing. Instead of relying on statistical correlations, the system uses trained neural networks to simulate interventions and directly measure causal effects, substituting a more powerful computational mechanism for the limited statistical approach.
2Measurement precision
If intervention testing is performed to accurately identify causal effects, then the precision of causal chain identification improves, but the complexity of the system increases due to multiple testing procedures
Solution Approach 1:
The patent uses copying by creating counterfactual versions of the system state through neural network simulations. Instead of performing physical interventions on the actual system, the method copies the system model and applies hypothetical interventions to these copies, measuring causal effects in the simulated environment without complicating the actual system.
Solution Approach 2:
The patent systematically changes parameters in the neural network model by applying different interventions to input variables. By varying these parameters in controlled ways and observing the effects on predictions, the method identifies causal relationships while managing complexity through structured parameter manipulation rather than uncontrolled system modifications.
3Measurement precision
If neural networks are used for sequence predictions, then the accuracy of predictions improves, but the interpretability of results deteriorates making it difficult to determine causal effects
Solution Approach 1:
The patent introduces feedback by using the neural network's own predictions as input for causal analysis. The system performs intervention testing on the trained network, using the predicted outputs to inform further causal investigations. This feedback loop allows the high-accuracy predictions to be systematically analyzed for causal content without sacrificing either accuracy or interpretability.
Data Source
AI summary
Described systems and techniques perform causal chain extraction for an investigated event in a system, using a neural network trained to represent a temporalsequence of events within the system. Such neural networks, by themselves, may be successful in predicting or characterizing system events, without providing useful interpretations of causation between the system events. Described techniques use the representational nature of neural networks to perform intervention testing using the neural network, distinguish confounding events, and identify a probabilistic root cause of the investigated event.