Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Evaluating online ad campaigns in a pipeline: causal models at scale
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 20th international conference on World wide web
Real-time bidding algorithms for performance-based display ad allocation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Targeting converters for new campaigns through factor models
Proceedings of the 21st international conference on World Wide Web
How effective is targeted advertising?
Proceedings of the 21st international conference on World Wide Web
Handling forecast errors while bidding for display advertising
Proceedings of the 21st international conference on World Wide Web
Dynamic evaluation of online display advertising with randomized experiments: an aggregated approach
Proceedings of the 22nd international conference on World Wide Web companion
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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In this paper, we develop an experimental analysis to estimate the causal effect of online marketing campaigns as a whole, and not just the media ad design. We analyze the causal effects on user conversion probability. We run experiments based on A/B testing to perform this evaluation. We also estimate the causal effect of the media ad design given this randomization approach. We discuss the framework of a marketing campaign in the context of targeted display advertising, and incorporate the main elements of this framework in the evaluation. We consider budget constraints, the auction process, and the targeting engine in the analysis and the experimental set up. For the effects of this evaluation, we assume the targeting engine to be a black box that incorporates the impression delivery policy, the budget constraints, and the bidding process. Our method to disaggregate the campaign causal analysis is inspired on randomized experiments with imperfect compliance and the intention-to-treat (ITT) analysis. In this framework, individuals assigned randomly to the study group might refuse to take the treatment. For estimation, we present a Bayesian approach and provide credible intervals for the causal estimates. We analyze the effects of 2 independent campaigns for different products from the Advertising.com ad network for 20M+ users each campaign.