US 2015/0310345 A1Published application

Modeling incrementaltreatment effect at individual levels using a shadow dependent variable

AssigneeBank of America Corporation

InventorXiaohu Liu

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Abstract

Embodiments of the invention are directed to systems, methods and computer program products for utilizing a shadow ridge rescaling technique to model incremental treatment effect at the individual level, based on a randomized test and control data. A shadow dependent variable is introduced with its mathematical expectation being exactly the incremental effect. Ridge regression is utilized to regress the shadow dependent variable on a set of variables generated from the test model score and the control model score. A tuning parameter in the ridge regression is selected so that the score can best rank order the incremental effect of the treatment. The final score is a nonlinear function of the test model score plus a nonlinear function of a control model score, and outperforms the traditional differencing score method.

Claims21

Dependent claims

A system for modeling incremental effect, the system comprising:

a memory device;
and
a processing device operatively coupled to the memory device, wherein the processing device is configured to execute computer-readable program code to:
split data for observations into development data and validation data;
create a test group model from the development data based on test group observations that are subject to a treatment;
create a control group model from the development data based on control group observations that are not subject to the treatment;
create a shadow dependent variable for the development data, wherein the shadow dependent variable is dependent on the test group observations, the control group observations, and a measurement performance variable;
score the development data by applying the test group model and the control group model to the development data;
create cubic spline basis functions for the test group model and the control group model;
standardize the shadow dependent variable and the cubic spline basis functions using the development data;
create a design matrix of the standardized shadow dependent variable and the cubic spline basis functions;
conduct a singular value decomposition on the design matrix;
utilize a binary search algorithm to determine tuning parameters for a set of degree of freedoms from the singular value decomposition;
calculate a parameter vector for each of the tuning parameters;
create a scoring formula based on the standardized cubic spline basis functions and the parameter vector for each of the tuning parameters;
calculate scores for each of the tuning parameters using the scoring formula and the validation data;
calculate an incremental effect area index of the scores for the tuning parameters values using the validation data;
identify a tuning parameter from the tuning parameters corresponding to a score from the scores that has a highest incremental effect area index;
and
wherein the tuning parameter with the score having the highest incremental effect area index is used to rank order an incremental effect of the treatment.

17 more elements

A computer program product for modeling incremental effect, the computer program product comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising:

an executable portion configured to split data for observations into development data and validation data;
an executable portion configured to create a test group model from the development data based on test group observations that are subject to a treatment;
an executable portion configured to create a control group model from the development data based on test group observations that fail to be subject to the treatment;
an executable portion configured to create a shadow dependent variable for the development data, wherein the shadow dependent variable is dependent on the test group observations, the control group observations, and a measurement performance variable;
an executable portion configured to score the development data by applying the test group model and the control group model to the development data;
an executable portion configured to create cubic spline basis functions for the test group model and the control group model;
an executable portion configured to standardize the shadow dependent variable and the cubic spline basis functions using the development data;
an executable portion configured to create a design matrix of the standardized shadow dependent variable and the cubic spline basis functions;
an executable portion configured to conduct a singular value decomposition on the design matrix;
an executable portion configured to utilize a binary search algorithm to determine tuning parameters for a set of degree of freedoms from the singular value decomposition;
an executable portion configured to calculate a parameter vector for each of the tuning parameters;
an executable portion configured to create a scoring formula based on the standardized cubic spline basis functions and the parameter vector for each of the tuning parameters;
an executable portion configured to calculate scores for each of the tuning parameters using the scoring formula and the validation data;
an executable portion configured to calculate an incremental effect area index of the scores for the tuning parameters values using the validation data;
an executable portion configured to identify a tuning parameter from the tuning parameters that has a highest incremental effect area index;
and
wherein the tuning parameter with the score having the highest incremental effect area index is used to rank order an incremental effect of the treatment.

14 more elements

A method for modeling incremental effect, the method comprising:

splitting, by a processor, data for observations into development data and validation data;
creating, by a processor, a test group model from the development data based on test group observations that are subject to a treatment;
creating, by a processor, a control group model from the development data based on test group observations that fail to be subject to the treatment;
creating, by a processor, a shadow dependent variable for the development data, wherein the shadow dependent variable is dependent on the test group observations, the control group observations, and a measurement performance variable;
scoring, by a processor, the development data by applying the test group model and the control group model to the development data;
creating, by a processor, cubic spline basis functions for the test group model and the control group model;
standardizing, by a processor, the shadow dependent variable and the cubic spline basis functions using the development data;
creating, by a processor, a design matrix of the standardized shadow dependent variable and the cubic spline basis functions;
conducting, by a processor, a singular value decomposition on the design matrix;
utilizing, by a processor, a binary search algorithm to determine tuning parameters for a set of degree of freedoms from the singular value decomposition;
calculating, by a processor, a parameter vector for each of the tuning parameters;
creating, by a processor, a scoring formula based on the standardized cubic spline basis functions and the parameter vector for each of the tuning parameters;
calculating, by a processor, scores for each of the tuning parameters using the scoring formula and the validation data;
calculating, by a processor, an incremental effect area index of the scores for the tuning parameters values using the validation data;
identifying, by a processor, a tuning parameter from the tuning parameters that has a highest incremental effect area index;
and
wherein the tuning parameter with the score having the highest incremental effect area index is used to rank order an incremental effect of the treatment.

14 more elements

Dependencies are inferred from the claim text.

Facts

Application: 14/263,543Filed 28 Apr 2014
Published: 29 Oct 2015
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G06Q 10/0637
Operations research, analysis or management: strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions…
G06F 17/18
Complex mathematical operations: for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression…
G06Q 10/063
Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling: operations research,…
G06Q 40/00
Finance