Causal Reasoning for Machine Learning Prediction Explanations
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Solution Overview
Problem
Existing deep learning and AI frameworks, such as LIME, assume features are independent, leading to incomplete or misleading explanations for model predictions, as they fail to account for causal relationships between dependent features, which is particularly problematic in applications like business processes where feature dependencies are inherent.
Innovation Solution
A system and method that determine causal relationships between feature data to generate multiple explanations for machine learning model predictions, using a processor-based system with components for determining dependencies, grouping features, sampling dataset points, and utilizing a classifier to sort features by contribution level, thereby providing detailed explanations of how dependent features influence predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If frameworks like LIME assume features are independent to simplify explanation generation, then the explanation process becomes easier and faster, but the accuracy and completeness of explanations deteriorate because causal relationships between dependent features are missed
Solution Approach 1:
The patent changes the fundamental parameter of feature independence assumption to feature dependency modeling. By representing features as nodes in a graph with edges indicating causal relationships, the system transforms the explanation approach from treating features as independent variables to modeling them as interconnected elements with directional dependencies, thereby capturing causal reasoning while maintaining computational feasibility
Solution Approach 2:
The patent introduces a graph-based intermediary structure that mediates between the machine learning model and the explanation generation process. This graph representation serves as a mediator that encodes domain knowledge about feature relationships, allowing the explanation framework to reason about causal relationships without directly analyzing complex feature interactions in the original data space
2Ease of operation
If frameworks highlight only the main feature affecting prediction to simplify explanations, then the explanation becomes more concise and easier to understand, but the completeness of causal reasoning is lost
Solution Approach 1:
The patent segments the explanation into multiple components by identifying different types of features in the causal graph: root causes (features with no incoming edges), intermediate features (features with both incoming and outgoing edges), and leaf features (features with outgoing edges but no further dependencies). This segmentation allows the system to present explanations at different levels of detail, highlighting main features while preserving causal reasoning information about how different feature types contribute to the prediction
Solution Approach 2:
The patent adds a dimensional structure to explanations by organizing features hierarchically according to their position in the causal graph. Instead of presenting a flat list of features, the system creates a multi-dimensional explanation structure that shows the causal pathway from root causes through intermediate features to the final prediction, enabling users to understand both the main drivers and the causal mechanisms
Data Source
AI summary
Techniques facilitating causal reasoning for explanation of model predictions are provided. A system can generate one or more explanations of a machine learning model prediction. The one or more explanations can be based on causal relationships determined between feature data of a set of feature data and based on dataset point samples around a trace associated with the causal relationships.