Mixed Causality Objective Function for Discrete and Continuous Variables
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Solution Overview
Problem
Current causal structure learning models are inadequate for handling mixed datasets containing both continuous and discrete variables, leading to poor performance and precision issues due to high time complexity and loss of information during discretization.
Innovation Solution
A method is introduced that determines a mixed causality objective function using weighted factors to adjust fitting inconsistency, allowing for optimal sparse causal inference across both continuous and discrete variables within a directed acyclic graph framework, thereby improving causality estimation precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If continuous variable discretization is used to process mixed variables in Bayesian network causal model, then the model can handle discrete variables, but information loss occurs and precision decreases
Solution Approach 1:
The patent segments the causal structure learning process into two distinct phases: equivalence class determination using conditional independence tests, and specific causality determination using fitting inconsistency. This segmentation allows each phase to use methods optimized for its purpose, avoiding the need to discretize continuous variables while still handling mixed variable types effectively.
Solution Approach 2:
The patent introduces fitting inconsistency as an intermediary metric that bridges the gap between equivalence class determination and specific causality determination. This intermediary allows the system to resolve ambiguities in causal directions without losing continuous variable information through discretization.
2Adaptability or versatility
If data type conversion based on conditional probability distribution is used, then uniform data type handling is achieved, but time complexity increases significantly
Solution Approach 1:
The patent segments the causal learning process into two phases: equivalence class determination (using conditional independence tests) and specific causality determination (using fitting inconsistency). This segmentation avoids the need for comprehensive data type conversions that would be required in a unified approach, thereby reducing computational time.
Solution Approach 2:
The patent applies partial action by using conditional independence tests only for determining equivalence classes, and then applying fitting inconsistency only for resolving specific causal directions. This partial application of different methods avoids the excessive computational burden of converting all data to a uniform format.
3Measurement precision
If conditional independence test is used to determine network structure, then equivalence class can be identified, but specific causality cannot be fully determined
Solution Approach 1:
The patent maintains continuity of useful action by seamlessly transitioning from equivalence class determination to specific causality determination. The fitting inconsistency metric continues the causal inference process started by conditional independence tests, resolving ambiguities and completing the causality determination without breaking the analytical chain.
Solution Approach 2:
The patent uses feedback by employing fitting inconsistency to evaluate and refine causal hypotheses generated from equivalence class determination. The fitting inconsistency provides feedback on which causal directions are more plausible, allowing the system to iteratively improve its causality estimates.
4Productivity
If existing sparse causal modeling methods are applied to mixed variables, then high-dimensional causal structure learning is attempted, but precision decreases due to discretization and equivalence class limitations
Solution Approach 1:
The patent segments the causal learning process into two phases: equivalence class determination using conditional independence tests, and specific causality determination using fitting inconsistency. This segmentation allows high-dimensional causal structure learning to proceed without the precision loss associated with discretization, as continuous variables are handled natively throughout the process.
Solution Approach 2:
The patent changes the parameter used for causal inference from discretized counts or converted probability distributions to fitting inconsistency, which is calculated directly from continuous data. This parameter change preserves information and improves precision in high-dimensional mixed variable settings.
Data Source
AI summary
In response to receiving observed data of mixed observed variables, a mixed causality objective function, being suitable for continuous observed variables and discrete observed variables is determined, wherein the mixed causality objective function includes a causality objective function for continuous observed variables and a causality objective function for discrete observed variables and the fitting inconsistency is adjusted based on weighted factors of the observed variables. Then, the mixed causality objective function is optimally solved by using a mixed sparse causal inference, suitable for both continuous observed variables and discrete observed variables, using the mixed observed data under a constraint of a directed acyclic graph, to estimate causality among the observed variables.