TensorFlow Residuals Isolate Driver Sensitivity in Causal Networks
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Solution Overview
Problem
Traditional methods struggle to determine the sensitivity of results to driver variables in causal networks, especially when confounders are present, making it difficult to identify optimal parameters and actions that maximize desired outcomes.
Innovation Solution
A computer-implemented method using TensorFlow models to determine sensitivities by negating initial outputs, generating residualized outputs, and optimizing weights based on a loss function to isolate the effect of driver variables on result variables, allowing for accurate identification of features to which the results are highly sensitive.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine sensitivity in causal networks, then the approach is simple, but the measurement precision is poor due to confounders and complex feedback loops
Solution Approach 1:
The patent introduces TensorFlow residual models as intermediary components that mediate between the causal graph representation and the sensitivity measurement. These residual models capture the complex relationships between driver variables and result variables, filtering out confounders and feedback loop effects to enable precise sensitivity measurement without requiring complex manual analysis of the entire causal network
Solution Approach 2:
The patent replaces traditional mechanical/mathematical sensitivity analysis methods with a machine learning-based approach using TensorFlow models. Instead of manually calculating sensitivities through complex causal graphs, the system uses neural networks to learn and represent causal relationships, automatically determining sensitivities through model training and residual analysis
2Reliability
If machine learning techniques are applied to causal graphs, then prediction accuracy improves, but determining independent effects becomes impossible for inter-correlated systems
Solution Approach 1:
The patent segments the causal graph into driver variables and result variables, and further divides the analysis into residual components. By representing the relationship between driver variables and result variables as residual models, the system can isolate and analyze the independent effects of each driver variable while accounting for correlations through the residualization process
Solution Approach 2:
The patent changes the parameters of the causal representation by using residualized values instead of raw values. The residual models transform the causal relationships into a form where independent effects can be identified through weight analysis, converting the problem of detecting independent effects in correlated systems into a parameter estimation problem that can be solved through machine learning optimization
3Adaptability or versatility
If multiple confounders are present in the causal graph, then the graph representation is comprehensive, but sensitivity determination becomes indeterminate
Solution Approach 1:
The patent uses residual models as intermediary representations that mediate between the comprehensive causal graph with confounders and the sensitivity measurement process. These residual models absorb the confounder effects during training, allowing the system to maintain comprehensive causal representation while achieving precise sensitivity measurements by analyzing the residual relationships
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for determining sensitivities of result variables to driver variables of a system. Various systems can be represented by a directed acyclic graph representation using nodes and edges to represent entities and relationships. A residual at the nodes can be determined to isolate the effect of a particular node on downstream nodes from the effect of upstream nodes on the particular node. High sensitivities to features associated with the system can be determined by weights that are components of a hyperparameter value resulting from optimizing an algorithmic sum of a loss function and the hyperparameter. Sensitivities to features can be used to present or prevent intervention using sensitive features.