Distributed Causal Inference via Coordination Node
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Solution Overview
Problem
Distributed computing systems face challenges in performing causal analysis on global datasets where data is partitioned and not accessible in its entirety due to privacy, sensitivity, or impracticality of transfer, necessitating a method to analyze data without moving or accessing the underlying data.
Innovation Solution
A coordination node coordinates multiple worker nodes to perform localized computing on dataset partitions, managing model convergence and bias through parameter provision and feedback systems, allowing analysis without transferring the data, by sending parameters, convergence indicators, and covariate indicators to worker nodes for iterative analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is centralized for analysis, then analysis accuracy is improved, but data privacy and transfer costs worsen
Solution Approach 1:
The patent segments the centralized data analysis task into distributed computations across multiple worker nodes. Each node processes local data partitions independently, computing local model updates (gradients, corrections) without exposing raw data. This segmentation enables accurate global analysis while preserving data privacy, as the coordination node only receives aggregated numerical updates rather than raw sensitive data.
2Measurement precision
If data is transferred for analysis, then comprehensive analysis is improved, but transfer costs and time worsen
Solution Approach 1:
The patent implements preliminary action by pre-distributing dataset partitions to worker nodes before the analysis task begins. This allows computation to occur locally on already-positioned data, eliminating the need for time-consuming data transfer during the analysis phase. The coordination node sends computation parameters and receives results without moving the actual data, significantly reducing transfer time while maintaining comprehensive analysis capability.
3Object-affected harmful factors
If distributed computing is used, then data privacy is improved, but coordination complexity worsens
Solution Approach 1:
The patent employs feedback mechanisms where worker nodes send computation results (local model updates, convergence indicators) back to the coordination node, which then adjusts global parameters and sends updated instructions. This structured feedback loop manages coordination complexity by establishing clear communication protocols and iterative refinement processes, enabling privacy-pres distributed computing without overwhelming system complexity.
Solution Approach 2:
The coordination node acts as an intermediary between worker nodes, managing all communication and computation coordination. It receives local updates from workers, aggregates them, computes global model updates, and distributes new parameters. This intermediary structure simplifies the distributed system by centralizing coordination logic, reducing direct peer-to-peer complexity while maintaining data privacy through the intermediary's controlled information exchange.
4Measurement precision
If iterative model convergence is enforced, then analysis accuracy is improved, but computational time worsens
Solution Approach 1:
The patent applies partial action by allowing iterative model convergence to proceed with approximate rather than perfect convergence at each step. The system performs a sufficient number of iterations to achieve acceptable accuracy without requiring full convergence, which would be computationally expensive. This balances analysis accuracy with computational throughput, preventing excessive iteration while maintaining useful model performance.
Data Source
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AI summary
A systems implements a gradient descent calculation, regression calculation, or other machine learning calculation on a dataset (e.g., a global dataset) using a coordination node including coordination circuitry that coordinates multiple worker nodes to create a distributed calculation architecture. In some cases, the worker nodes each hold a portion of the dataset and operate on their respective portion. In some cases, the gradient descent calculation, regression calculation, or other machine learning calculation is used to implement a targeted maximum likelihood scheme for causal inference estimation. The targeted maximum likelihood scheme may be used to conduct causal analysis of the observational data.