Causally Motivated Attribution for Online Advertising

  • Brian Dalessandro
  • Claudia Perlich
  • Foster Provost
  • Ori Stitelman

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.