Distributed Causal Inference Stack for Privacy-Preserving TMLE
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Solution Overview
Problem
Existing computational systems face challenges in efficiently and accurately performing targeted maximum likelihood calculations due to computational complexity and large data volumes, while also requiring secure handling of sensitive data and ensuring data availability across distributed locations.
Innovation Solution
A distributed calculation architecture utilizing localized data handling and coordination nodes to perform causal inference, which includes privacy-preserving data transformation, localized data analysis, and bias management, allowing analysis on distributed datasets without moving the data, and improving calculation reliability through predicate condition verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized data processing is used to perform targeted maximum likelihood calculations, then calculation accuracy can be maintained, but computational complexity and data transfer requirements increase significantly
Solution Approach 1:
The patent segments the centralized calculation task into distributed local calculations across multiple nodes. Each node performs targeted maximum likelihood calculations on its local data partition independently, then results are aggregated. This segmentation reduces computational complexity at any single node while maintaining overall calculation accuracy through coordinated distributed processing.
2Measurement precision
If data is transferred to a centralized location for analysis, then comprehensive causal inference can be performed, but data security and privacy protection are compromised
Solution Approach 1:
The patent segments the dataset into distributed partitions stored at different locations, preventing centralized data aggregation. Each location performs local causal inference calculations on its partition, and results are combined to achieve comprehensive inference without exposing sensitive raw data, thus maintaining data security while preserving analytical accuracy.
Solution Approach 2:
The patent introduces an intermediary result aggregation mechanism where only calculation results (not raw data) are transferred between nodes. This intermediary layer enables comprehensive causal inference by combining local results while preventing direct exposure of sensitive data, resolving the conflict between inference accuracy and data security.
3Object-affected harmful factors
If distributed data processing is implemented to improve data security, then data transfer needs are reduced, but calculation coordination complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where nodes exchange information about their local calculation results and data characteristics. This feedback enables automatic coordination of the distributed processing, allowing the system to manage coordination complexity through iterative result aggregation and model updating without requiring complex centralized control.
4Productivity
If more computing resources are allocated to handle large data volumes, then calculation speed improves, but operational cost and resource consumption increase
Solution Approach 1:
The patent segments the large-scale calculation task into parallel distributed computations across multiple nodes, each handling local data partitions. This segmentation enables efficient utilization of available computing resources without requiring excessive concentration of resources at a single location, improving overall calculation speed while distributing resource consumption across the network.
Data Source
AI summary
A causal inference stack implements a targeted maximum likelihood scheme to conduct causal analysis of the observational data. At a data-handling layer, the causal inference stack obtains one or more memory locations for a dataset and establishes analysis nodes to setup localized data handling for the memory locations. At a data classification layer, the causal inference stack characterizes the missingness of the dataset. At a pipeline layer, the causal inference stack obtains a data element dependency query from a user and sets up an end-to-end solution path to determine the presence of a causal relationship between data elements identified in the data element dependency query.