The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 02, 2021

Filed:

Dec. 13, 2018
Applicant:

The Nielsen Company (Us), Llc, New York, NY (US);

Inventors:

Michael Sheppard, Holland, MI (US);

Ludo Daemen, Duffel, BE;

Edward Murphy, North Stonington, CT (US);

Remy Spoentgen, Tampa, FL (US);

Assignee:

The Nielsen Company (US), LLC, New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/00 (2012.01); G06Q 30/02 (2012.01); G06F 17/15 (2006.01); G06F 17/18 (2006.01);
U.S. Cl.
CPC ...
G06Q 30/0244 (2013.01); G06F 17/15 (2013.01); G06Q 30/0245 (2013.01); G06F 17/18 (2013.01);
Abstract

Methods, systems, apparatus and articles of manufacture to determine causal effects are disclosed herein. An example apparatus includes a weighting engine to calculate a first set of weights for a first set of covariates corresponding to a treatment dataset and a second set of weights for a second set of covariates corresponding to a control dataset using maximum entropy, the first set of weights to equal the second set of weights. The example apparatus also includes a weighting response engine to calculate a weighted response for the treatment dataset and a weighted response for the control dataset by: mapping the first set of weights and the second set of weights to a uniform weighting identifier, determining a constraint matrix based on the first set of weights, the second set of weights and the uniform weighting identifier, and bypassing multivariate reweighting by calculating the weighted response for the treatment dataset and the weighted response for the control dataset by applying maximum entropy to the constraint matrix.


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