NDLI: Causally motivated attribution for online advertising

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Causally motivated attribution for online advertising

Content Provider	ACM Digital Library
Author	Stitelman, Ori Provost, Foster Dalessandro, Brian Perlich, Claudia
Abstract	In many online advertising campaigns, multiple vendors, publishers or search engines (herein called channels) are contracted to serve advertisements to internet users on behalf of a client seeking specific types of conversion. In such campaigns, individual users are often served advertisements by more than one channel. The process of assigning conversion credit to the various channels is called "attribution," and is a subject of intense interest in the industry. This paper presents a causally motivated methodology for conversion attribution in online advertising campaigns. We discuss the need for the standardization of attribution measurement and offer three guiding principles to contribute to this standardization. Stemming from these principles, we position attribution as a causal estimation problem and then propose two approximation methods as alternatives for when the full causal estimation can not be done. These approximate methods derive from our causal approach and incorporate prior attribution work in cooperative game theory. We argue that in cases where causal assumptions are violated, these approximate methods can be interpreted as variable importance measures. Finally, we show examples of attribution measurement on several online advertising campaign data sets.
Starting Page	1
Ending Page	9
Page Count	9
File Format	PDF
ISBN	9781450315456
DOI	10.1145/2351356.2351363
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Variable importance Causal estimation Digital advertising Attribution
Content Type	Text
Resource Type	Article

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