SELECTION BIAS CORRECTION FOR PAID SEARCH IN MEDIA MIX MODELING
First Claim
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1. A method comprising:
- identifying, by one or more processors, a first Uniform Resource Locator (URL) associated with an incremental value change of a first metric;
identifying, by the one or more processors, a plurality of URLs associated with the first URL;
receiving search query data comprising queries from a target geographical region in a first time window;
partitioning, by the one or more processors, the received search query data to a plurality of groups comprising a first group associated with the first URL, a second group associated with one or more of the plurality of URLs, and a third group associated with a business category, each respective association defined by equaling or exceeding a predetermined threshold of a second metric;
generating, by the one or more processors, a plurality of search query subsets based on the plurality of groups;
generating, by the one or more processors, an additive regression model based on a causal diagram that comprises as identification of a causal effect associated with the incremental value change of the first metric; and
calculating, by the one or more processors, a bias corrected estimate of the incremental value change of the first metric by fitting the additive regression model to the plurality of search query subsets.
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Abstract
Systems, methods, and computer-readable storage media that may be used to generate causal models and calculate a selection bias in mixed media. In some embodiments, the selection bias calculation is in search sponsored content in the context of mixed media modeling. In some embodiments, a method for search bias correction is based on the back-door criterion from causal inference.
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Citations
20 Claims
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1. A method comprising:
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identifying, by one or more processors, a first Uniform Resource Locator (URL) associated with an incremental value change of a first metric; identifying, by the one or more processors, a plurality of URLs associated with the first URL; receiving search query data comprising queries from a target geographical region in a first time window; partitioning, by the one or more processors, the received search query data to a plurality of groups comprising a first group associated with the first URL, a second group associated with one or more of the plurality of URLs, and a third group associated with a business category, each respective association defined by equaling or exceeding a predetermined threshold of a second metric; generating, by the one or more processors, a plurality of search query subsets based on the plurality of groups; generating, by the one or more processors, an additive regression model based on a causal diagram that comprises as identification of a causal effect associated with the incremental value change of the first metric; and calculating, by the one or more processors, a bias corrected estimate of the incremental value change of the first metric by fitting the additive regression model to the plurality of search query subsets. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system comprising:
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at least one computing device operably coupled to at least one memory and configured to; identify a first Uniform Resource Locator (URL) associated with an incremental value change of a first metric; identify a plurality of URLs associated with the first URL; receive search query data comprising queries from a target geographical region in a first time window; partition the received search query data to a plurality of groups comprising a first group associated with the first URL, a second group associated with one or more of the plurality of URLs, and a third group associated with a business category, each respective association defined by equaling or exceeding a predetermined threshold of a second metric; generate a plurality of search query subsets based on the plurality of groups; generate an additive regression model based on a causal diagram that comprises as identification of a causal effect associated with the incremental value change of the first metric; and calculate a bias corrected estimate of the incremental value change of the first metric by fitting the additive regression model to the plurality of search query subsets. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-readable media having computer-executable instructions embodied therein that, when executed by one or more processors of a computing system, cause the computing system to perform a process comprising:
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identifying a first content item; determining a media mix environment is associated with the first content item; determining a causal relationship between a first media of the media mix environment and a second media of the media mix environment; identifying a first Uniform Resource Locator (URL) associated with the first content item; identifying a plurality of URLs associated with the first URL; receiving search query data comprising queries from a target geographical region in a first time window; partitioning the received search query data to a plurality of groups comprising a first group associated with the first URL, a second group associated with one or more of the plurality of URLs, and a third group associated with a business category, each respective association defined by equaling or exceeding a predetermined threshold of a second metric; generating a plurality of search query subsets based on the plurality of groups; calculating a bias factor between the first media of the media mix environment and the second media of the media mix environment based on the causal relationship; generating an additive regression model using the bias factor between the first media of the media mix environment and the second media of the media mix environment; and calculating a bias corrected estimate of the incremental value change of the first metric by fitting the additive regression model to the plurality of search query subsets. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification