Knowledge Graph Embeddings for Accurate Causal Effect Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional approaches for causal inference from knowledge graphs are limited in scope and efficiency, and machine learning methods for estimating causal effects are not sufficiently accurate.
Innovation Solution
A method and system utilizing a neural-network-based intervention stack to map covariate value sets onto knowledge graphs, translating these into matrices, and comparing subgroups to determine differential intervention effects, enhancing accuracy and reliability of causal effect estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional causal inference approaches are used, then the method is simple to implement, but the accuracy and reliability of causal effect estimation is limited
Solution Approach 1:
The patent replaces traditional mechanical/statistical causal inference methods with a neural network-based system. The neural network learns complex patterns and relationships in the knowledge graph that traditional methods cannot capture, thereby improving measurement precision of causal effects while accepting increased system complexity.
Solution Approach 2:
The patent creates a composite estimation system that integrates multiple components: knowledge graph data, neural network models, intervention stack, and counterfactual reasoning. This composite approach combines different data representations and processing methods to achieve higher accuracy in causal effect estimation than any single method could provide alone.
2Measurement precision
If machine learning models are used for causal estimation, then the estimation accuracy improves, but the computational efficiency and processing speed decrease
Solution Approach 1:
The patent performs preliminary actions by pre-processing the knowledge graph into structured formats, pre-training neural network models on available data, and pre-computing embeddings for entities and relationships. This preliminary work reduces the computational burden during actual causal effect estimation, improving productivity while maintaining accuracy.
Solution Approach 2:
The patent segments the causal estimation process into distinct tiers: the classification tier for data organization, the knowledge tier for neural network processing, and the matrix tier for computation. This segmentation allows each tier to be optimized independently, balancing accuracy requirements with computational efficiency at each stage.
3Reliability
If a detailed intervention stack with multiple tiers is implemented, then the reliability of causal effect determination improves, but the device complexity and implementation difficulty increase
Solution Approach 1:
The intervention stack is segmented into three distinct tiers: classification tier for organizing intervention data, knowledge tier for neural network-based causal reasoning, and matrix tier for computational processing. This segmentation improves reliability by ensuring each tier performs its specific function correctly while making the overall complex system more manageable through clear separation of concerns.
Data Source
AI summary
Methods and systems for estimating causal effects from knowledge graphs are provided. The method includes obtaining intervention application data and subject history data for a candidate group of subjects, and dividing, based on the intervention application data, the candidate group into a reception subgroup that received an intervention and a rejected subgroup that did not receive the intervention. The method includes for each subject within the candidate group, mapping, based on the subject history data, a covariate value set onto a knowledge graph with an embedding neural network; and for each subject in the reception subgroup or the rejection subgroup, translating the covariate value sets for the subjects within the reception subgroup or the rejection subgroup into a reception matrix or a rejection matrix with a feature neural network. The method includes comparing the reception subgroup to the rejection subgroup to determine a differential intervention effect.