Propensity Score Causal Modeling for Credit Estimation
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Solution Overview
Problem
Conventional credit decision estimation techniques rely on intuition and multiple covariates, leading to selection bias and inaccuracies, as they fail to accurately represent the effects of multiple variables with a single variable, and do not align with empirical historical results.
Innovation Solution
A causal modeling approach using a propensity score that encompasses the effects of multiple covariates, based on assumptions of unconfoundedness and localized common support, to estimate potential outcomes from decision alternatives, allowing for a single dimension representation of multiple covariates and reducing selection bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional estimation techniques using multiple covariates are used, then the estimation can capture individual differences, but selection bias and inaccuracies occur
Solution Approach 1:
The patent introduces a propensity score as an intermediary variable that mediates between multiple covariates and the outcome. This single score encapsulates the combined effect of multiple covariates (income, age, credit score, etc.), allowing the model to handle complexity while maintaining estimation accuracy and reducing selection bias.
Solution Approach 2:
The patent transforms multiple covariate parameters into a single propensity score parameter through statistical modeling. This parameter transformation reduces the dimensionality of the problem from multiple variables to one composite variable, simplifying the model while preserving the essential information needed for accurate estimation.
2Ease of operation
If intuition-based estimation is used, then the process is simple and convenient, but the results do not conform to empirical historical results
Solution Approach 1:
The patent replaces intuition-based mechanical estimation with a data-driven causal modeling system. The propensity score methodology uses empirical historical data to establish causal relationships, substituting subjective judgment with objective statistical analysis that conforms to empirical results while maintaining operational simplicity through automated computation.
3Loss of information
If multiple covariates are used to represent individual characteristics, then the representation is comprehensive, but the analysis becomes complex and difficult to manage
Solution Approach 1:
The patent merges multiple covariate variables into a single propensity score that represents the combined effect of all covariates. This consolidation maintains the comprehensive information from multiple sources (income, age, credit score, etc.) while simplifying the analysis to a single dimension, making the model manageable without losing essential information.
Data Source
AI summary
A method and system for estimating potential future outcomes resulting from decision alternatives is presented to enable lenders to make lending related decisions. The estimation is based on a propensity score variable that encompasses an effect of multiple covariates associated with one or more individuals for whom the estimation is being performed. For consistency with empirical testing, the estimation approach assumes conditions of unconfoundedness and localized common support. According to the unconfoundedness assumption, for a given variable, the potential outcomes are conditionally independent of the decision alternatives. According to the localized common support assumption, an overlap is ensured between individual accounts that are categorized together as potentially having the same future outcome. The outcomes and an effect (e.g. comparison) of the outcomes may be displayed graphically.