Causal Content Distribution Using Confounder-Controlled Graph Learning
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Solution Overview
Problem
Conventional data processing systems struggle with accurately tracking causal relationships among data features due to the limitations of correlation analysis and Bayesian statistical methods, which are inaccurate, inefficient, and not scalable, hindering proactive decision-making about content provision.
Innovation Solution
A machine learning model is employed to iteratively optimize edges of graphs, where nodes represent data features and edge weights indicate causal relationship strengths, allowing for more accurate and scalable content provision based on causal relationship data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If correlation analysis is used to determine causal relationships, then the analysis can be performed without controlling for confounders, but the accuracy of the causal relationship determination deteriorates
Solution Approach 1:
The patent introduces an intermediary variable (confounder) that mediates the relationship between independent and dependent variables. By identifying and controlling for these intermediary factors through the machine learning model, the system accurately determines true causal relationships rather than spurious correlations, thereby resolving the contradiction between ease of analysis and accuracy.
2Productivity
If Bayesian statistical methods are used to perform proactive data analysis, then predictions can be generated, but the methods become inaccurate, inefficient, and not scalable due to reliance on assumptions and pre-specifications
Solution Approach 1:
The patent replaces the mechanical statistical system (Bayesian methods with fixed assumptions) with a machine learning-based causal inference system. This substitution enables the model to automatically learn causal structures from data without relying on pre-specified assumptions, thereby simultaneously improving both accuracy and scalability of predictive analytics.
Solution Approach 2:
The patent introduces dynamic adaptability by allowing the machine learning model to automatically adjust to different data distributions and causal structures without requiring re-specification of assumptions. This dynamic approach enables scalable application across diverse domains while maintaining prediction accuracy, resolving the contradiction between efficiency and precision.
3Reliability
If conventional data processing techniques are used to track data values, then progress towards goals can be monitored, but the ability to make proactive decisions deteriorates due to difficulty in tracking causal relationships
Solution Approach 1:
The patent uses the machine learning model as an intermediary that automatically identifies and tracks causal relationships between data features. This intermediary system transforms raw data tracking into actionable causal insights, enabling reliable goal monitoring while simultaneously making proactive decision-making easy through automated causal inference.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for data processing include obtaining data from a software application, where the data includes one or more of content data, interaction data, profile data, and factor data, generating shadow data corresponding to the data by duplicating the data and randomly reassigning feature values of the duplicated data, selecting one or more prominent features by comparing the data and the shadow data, computing causal relationship data for the data by optimizing a plurality of edges on one or more graphs based on the one or more prominent features, and providing content to a user via the software application based on the causal relationship data.