Propensity Score Analysis via Principal Component Transformation
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Solution Overview
Problem
Propensity score analysis faces challenges with multicollinearity issues when estimating covariates, particularly as the number of covariates increases, necessitating the exclusion of correlated variables to prevent multicollinearity without reducing estimation accuracy.
Innovation Solution
An analysis apparatus that converts correlated covariates into uncorrelated variables using principal component analysis, allowing for the computation of propensity scores while maintaining relationships between covariates, thereby preventing multicollinearity and enabling robust causal inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of covariates is increased to improve the robustness of causal inference, then the estimation accuracy is improved, but multicollinearity occurs among the covariates
Solution Approach 1:
The patent transforms the original correlated covariates into new parameters (principal components) with different statistical properties. By changing the parameter representation from correlated covariates to uncorrelated principal components, the system maintains the information content while eliminating multicollinearity, thus resolving the contradiction between estimation accuracy and reliability
2Reliability
If correlated covariates are excluded to prevent multicollinearity, then multicollinearity is prevented, but the number of covariates is reduced
Solution Approach 1:
Instead of excluding correlated covariates, the patent transforms them into a new set of parameters through principal component analysis. This parameter transformation maintains the quantity of information while changing its form, allowing all original covariate information to be preserved in the transformed space without multicollinearity
Solution Approach 2:
The patent creates composite parameters (principal components) that combine multiple original covariates. Each principal component is a composite of several correlated covariates, allowing the system to retain information from all original variables while the composite structure eliminates pairwise correlations between the new parameters
Data Source
AI summary
An analysis apparatus for analyzing a causal relationship between an incidence of a predetermined disease and a predetermined intervention includes a memory; and a processor configured to execute: converting a plurality of first parameters indicative of attributes of users belonging to a population, at least two of the parameters having correlations with a predetermined strength, to a plurality of second parameters without the correlations with the predetermined strength with each other; computing a predetermined score for each of the users, using the plurality of second parameters and a parameter indicative of presence or absence of the intervention; and clustering the users belonging to the population using the score, to analyze the causal relationship.