Root Cause Subroutines Using Feature Contribution Shifts
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Solution Overview
Problem
Conventional root cause analysis techniques fail to adequately identify the specific contributions of different variables, leading to incomplete understanding and ineffective solutions, particularly in sectors like healthcare where factors such as staff training and system design flaws are overlooked.
Innovation Solution
A system that utilizes feature contributions and partial dependence plots to identify principal drivers of change by analyzing baseline and updated datasets, enabling clear identification of variables contributing to shifts in a value of interest through machine learning models and model interpretability methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional root cause analysis techniques are used, then the analysis process is simple, but the identification of specific variable contributions is inadequate
Solution Approach 1:
The patent introduces model interpretability methods (such as SHAP, LIME, or feature attribution techniques) as intermediary tools between the machine learning model and the root cause analysis process. These intermediaries decompose the complex model predictions into quantifiable feature contributions, enabling precise identification of which variables drove the shift in target values without requiring the user to understand the underlying model complexity.
Solution Approach 2:
The patent replaces conventional mechanical root cause analysis methods (manual inspection, statistical correlation) with machine learning models and their interpretability frameworks. This substitution enables automated, scalable analysis that can handle nonlinear relationships and high-dimensional data while providing precise feature contribution measurements through mathematical decomposition techniques.
2Loss of information
If conventional root cause analysis techniques are used, then the implementation is straightforward, but the understanding of contributing factors is incomplete
Solution Approach 1:
The patent implements a feedback mechanism where the system generates explanations by tracing back model predictions to individual feature contributions. The interpretability methods provide feedback loops that quantify how each feature influenced the prediction shift, allowing users to iteratively explore and understand the complete set of contributing factors rather than relying on incomplete conventional analysis.
Solution Approach 2:
The patent adds a new dimension of analysis by introducing feature contribution scores and attribution values alongside traditional root cause identification. This dimensional expansion transforms the analysis from simply identifying potential causes to quantifying the precise magnitude and direction of each feature's influence, providing a more complete information picture about contributing factors.
3Adaptability or versatility
If feature segmentation is required for analysis, then the analysis can be simplified, but the applicability to different data types is limited
Solution Approach 1:
The patent employs machine learning models with universal interpretability methods that can analyze both linear and nonlinear relationships without requiring data transformation or segmentation. The interpretability frameworks (such as gradient-based methods or permutation importance) are designed to work across different model types and data distributions, providing a unified approach that maintains high adaptability while managing complexity through standardized procedures.
Data Source
AI summary
Methods and systems are described herein for identifying one or more variables as key drivers of shift in a value of interest. In some aspects, a root cause analysis system may implement a subroutine to use feature contributions to identify the contributions of each variable in a baseline dataset and an updated dataset, where a value of interest has shifted between the baseline and updated datasets. By taking the difference in feature contributions between the datasets, the system may identify those variables with the largest shift in contribution as principal drivers of change for a value of interest. In some aspects, a root cause analysis system may implement a subroutine to generate partial dependence plots (PDPs) for each feature. By comparing different PDPs for each feature, the system may identify the features with significantly different feature-target relationships and find the key segments responsible for performance change.