A Bayesian network framework for reject inference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 20th international conference on World wide web
Display advertising impact: search lift and social influence
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Impact of ad impressions on dynamic commercial actions: value attribution in marketing campaigns
Proceedings of the 21st international conference companion on World Wide Web
Design principles of massive, robust prediction systems
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Marketing campaign evaluation in targeted display advertising
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
Causally motivated attribution for online advertising
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
Measuring dynamic effects of display advertising in the absence of user tracking information
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
Impact of spam exposure on user engagement
Security'12 Proceedings of the 21st USENIX conference on Security symposium
Dynamic effects of ad impressions on commercial actions in display advertising
Proceedings of the 21st ACM international conference on Information and knowledge management
An efficient framework for online advertising effectiveness measurement and comparison
Proceedings of the 7th ACM international conference on Web search and data mining
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Display ads proliferate on the web, but are they effective? Or are they irrelevant in light of all the other advertising that people see? We describe a way to answer these questions, quickly and accurately, without randomized experiments, surveys, focus groups or expert data analysts. Doubly robust estimation protects against the selection bias that is inherent in observational data, and a nonparametric test that is based on irrelevant outcomes provides further defense. Simulations based on realistic scenarios show that the resulting estimates are more robust to selection bias than traditional alternatives, such as regression modeling or propensity scoring. Moreover, computations are fast enough that all processing, from data retrieval through estimation, testing, validation and report generation, proceeds in an automated pipeline, without anyone needing to see the raw data.