Discretizing Numerical Variables for Cause-Effect Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to estimate cause-effect relationships between multiple variables, particularly when categorical and numerical variables are mixed, as they face difficulties in assigning labels and determining whether to discretize numerical variables based on categorical variables, leading to challenges in building models like partial ancestral graphs.
Innovation Solution
An information processing apparatus and method that discretizes numerical variables based on categorical variables within a graphical model, using a discretization section to create discrete variables for conditional independence testing, allowing for the estimation of cause-effect relationships even when categorical and numerical variables are mixed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conditional independence testing is performed with mixed categorical and numerical variables, then cause-effect relationship estimation becomes possible, but difficulty in assigning labels and determining discretization arises
Solution Approach 1:
The patent segments the variable processing by distinguishing between categorical variables and numerical variables. Numerical variables are discretized into categorical forms based on categorical variables, allowing the system to handle mixed variable types through separate processing paths that ultimately integrate in the graphical model construction.
Solution Approach 2:
The patent changes the parameter state of numerical variables by transforming them from continuous values to discrete categorical values through discretization. This parameter transformation enables the application of conditional independence testing methods designed for categorical variables to mixed variable sets, resolving the complexity of handling different variable types simultaneously.
2Adaptability or versatility
If numerical variables are discretized based on categorical variables, then conditional independence testing becomes feasible, but additional processing steps are required
Solution Approach 1:
The patent performs preliminary discretization of numerical variables based on categorical variables before conducting conditional independence testing. This preliminary action prepares the data in a uniform format that facilitates subsequent testing and model construction, eliminating the need to handle different variable types during the main processing phase.
Solution Approach 2:
The patent introduces discretization as an intermediary process that converts numerical variables into categorical variables. This intermediary transformation enables the direct application of categorical variable testing methods to mixed variable sets, serving as a bridge between different variable type processing requirements.
3Reliability
If discrete variables are used for testing, then conditional independence can be tested effectively, but information loss during discretization may occur
Solution Approach 1:
The patent applies local quality by performing discretization selectively based on the specific requirements of conditional independence testing. Rather than uniformly discretizing all numerical variables, the method discretizes only when and where categorical variables serve as conditioning variables, preserving numerical precision where it maintains test reliability.
Solution Approach 2:
The patent applies partial discretization rather than complete discretization of all numerical variables. By discretizing only the portions of numerical variables that require categorical form for specific testing scenarios, the method achieves sufficient test reliability while minimizing information loss in the overall dataset.
Data Source
AI summary
Provided is an information processing apparatus that tests independence between multiple variables, the information processing apparatus including: a discretization section that discretizes at least one numerical variable on the basis of at least one categorical variable, when the categorical variable and the numerical variable are included in at least two dependent variables in a graphical model and a set of conditional variables serving as conditions of independence between the two variables; and a test execution section that executes a test for conditional independence between the two variables by using the categorical variable and a discrete variable which is obtained by discretizing the numerical variable.