Causal Inference Impact Measures for Resource Allocation
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Solution Overview
Problem
Existing resource distribution techniques fail to accurately account for incomplete or inaccurate attributes of data objects, leading to inefficiencies and inaccuracies in predicting causal effects and optimizing resource allocation.
Innovation Solution
The use of deterministic rules and machine learning models to generate cohorts of data objects associated or unassociated with predictive labels, and to calculate impact measures, allowing for tailored resource distribution based on causal inferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional resource distribution techniques are used, then the process is simple and fast, but the accuracy of causal effect predictions deteriorates due to incomplete or inaccurate attributes
Solution Approach 1:
The patent segments the data objects into multiple cohorts (positive cohorts with predictive labels, negative cohorts without predictive labels, positive risk-based cohorts, and negative risk-based cohorts) based on different criteria. This segmentation allows the system to handle incomplete or inaccurate attributes by creating distinct groups that can be processed with appropriate models, thereby improving prediction accuracy while managing complexity through structured organization.
Solution Approach 2:
The patent changes the parameters used for resource distribution from simple attribute-based classification to a multi-parameter system involving predictive labels, risk scores, and causal inference models. By introducing these additional parameters and using nonlinear causal inference modeling, the system achieves more accurate causal effect predictions while the structured approach to parameter management keeps the complexity可控.
2Reliability
If deterministic rules are used to generate cohorts, then the process is transparent and controllable, but the ability to handle incomplete or inaccurate attributes deteriorates
Solution Approach 1:
The patent introduces risk scores as an intermediary mechanism that bridges deterministic rules and machine learning models. Risk scores are generated using machine learning prediction models to handle incomplete or inaccurate attributes, while the overall cohort generation process maintains transparency through deterministic rules. This intermediary layer allows the system to improve reliability in handling data quality issues while preserving ease of operation through interpretable risk scoring.
Solution Approach 2:
The patent performs preliminary actions by pre-generating risk scores for data objects before final cohort assignment. This preliminary computation using machine learning models allows the system to prepare handling for incomplete or inaccurate attributes in advance, while the subsequent deterministic cohort generation maintains transparency and controllability. The preliminary risk scoring step enables the system to address data quality issues proactively.
3Measurement precision
If machine learning prediction models are used to generate risk scores, then the accuracy of predictive labels improves, but the computational resources required increase
Solution Approach 1:
The patent segments the population into different cohorts and applies machine learning prediction models selectively only where needed (for generating risk scores for negative cohorts and object impact measures). This segmented approach allows the system to improve predictive label accuracy for specific subsets of data objects while minimizing overall computational resource consumption by avoiding unnecessary model applications across the entire dataset.
Solution Approach 2:
The patent applies different levels of computational complexity to different parts of the system. Machine learning prediction models are used locally for generating risk scores and object impact measures where predictive accuracy is most critical, while simpler deterministic rules are used for straightforward cohort generation. This local quality approach optimizes the balance between predictive label accuracy and computational resource consumption by applying complex models only where they provide the most value.
4Productivity
If resource distribution is optimized for specific cohorts, then the efficiency of resource allocation improves, but the complexity of managing multiple cohort types increases
Solution Approach 1:
The patent segments data objects into distinct cohort types (positive cohorts, negative cohorts, positive risk-based cohorts, negative risk-based cohorts) with specific resource distribution strategies. This segmentation enables the system to optimize resource distribution efficiency for each cohort type using appropriate models and methods, while the structured segmentation approach actually reduces management complexity by providing clear categorization and standardized handling procedures for each cohort type.
Data Source
AI summary
Various embodiments of the present disclosure provide techniques for improving causal inference modeling with respect to predictive labels in a complex predictive domain. The techniques of the present disclosure may include generating a positive cohort and a negative cohort of data objects from a dataset based on an associated with a predictive label, generating a positive cohort impact measure for the positive cohort and one or more negative cohort impact measures for the negative cohort, and generating an object impact measure for a particular data object of the negative cohort based on the positive cohort impact measure and at least one of the negative cohort impact measures.