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In this paper, we propose an offline counterfactual policy estimationframework called Genie to optimize Sponsored Search Marketplace. Genie employsan open box simulation engine with click calibration model to compute the KPIimpact of any modification to the system. From the experimental results on Bingtraffic, we showed that Genie performs better than existing observationalapproaches that employs randomized experiments for traffic slices that havefrequent policy updates. We also show that Genie can be used to tune completelynew policies efficiently without creating risky randomized experiments due tocold start problem. As time of today, Genie hosts more than 10000 optimizationjobs yearly which runs more than 30 Million processing node hours of big datajobs for Bing Ads. For the last 3 years, Genie has been proven to be the one ofthe major platforms to optimize Bing Ads Marketplace due to its reliabilityunder frequent policy changes and its efficiency to minimize risks in realexperiments.

Genie: An Open Box Counterfactual Policy Estimator for Optimizing Sponsored Search Marketplace