System and method for developing a propensity model

Details System and method for developing a propensity model System and method for developing a propensity model

2008-01-08

2011-11-08

Gaffin, Jeffrey A (Department: 2129)

Data processing: artificial intelligence

Machine learning

Genetic algorithm and genetic programming system

C706S012000, C706S062000

Reexamination Certificate

active

08055596

ABSTRACT:
A technique is provided for developing a propensity model for customer behavior. Multiple biased samples of customer characteristics and results from past activities are established. Initial propensity models are created for each biased sample. The propensity models established for each biased sample are processed separately from the propensity models established for the other biased samples. A genetic algorithm is used to evolve the propensity models. A select number of propensity models that best fit their respective biased samples are compared to a validation sample that is unbiased. A select number of these propensity models that best fit the validation sample are cross-bred into the propensity models established for each biased sample. The propensity models for each biased sample are then processed again using the genetic algorithms. However, a number of elite propensity models are maintained in their original form and not evolved using the genetic algorithm. This cycle continues until a stopping criterion is reached.

REFERENCES:
patent: 7801839 (2010-09-01), Kates et al.
patent: 2005/0234688 (2005-10-01), Pinto et al.
Diamond et al., A., “Genetic Matching for Estimating Causal Effect: A General Multivariate Matching Method for Achieving Balance in Observational Studies”, Midwest Political Science Association, pp. 1-45, Apr. 7-10, 2005.
Lee, W., “Propensity Score Matching and Variations on the Balancing Test”, Melbourne Institute of Applied Economic and Social Research, pp. 1-48, Nov. 3, 2005.
Sekhon, J., “Muiltivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R”. Journal of Statistical Software, pp. 1-47, 2007.
Joffe et al., M., “Invited Commentary: Propensity Scores”, American Journal of Epidemiology, pp. 1-7, Aug. 15, 1999.
Sekhon, J., “Alternative Balance Metrics for Bias Reduction in Matching Methods for Causal Inference”, pp. 1-21, Jan. 16, 2007.

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