2010 EvaluatingOnlineAdCampaignsinaP

• (Chan et al., 2010) ⇒ David Chan, Rong Ge, Ori Gershony, Tim Hesterberg, and Diane Lambert. (2010). “Evaluating Online Ad Campaigns in a Pipeline: Causal Models at Scale.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835809

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Evaluating+online+ad+campaigns+in+a+pipeline%3A+causal+models+at+scale%22+2010
• http://portal.acm.org/citation.cfm?id=1835809&preflayout=flat#citedby

Quotes

Author Keywords

Ad effectiveness, display ads, doubly robust estimation, irrelevant outcomes, observational data, propensity score, selection bias

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 EvaluatingOnlineAdCampaignsinaP	David Chan Rong Ge Ori Gershony Tim Hesterberg Diane Lambert			Evaluating Online Ad Campaigns in a Pipeline: Causal Models at Scale		KDD-2010 Proceedings		10.1145/1835804.1835809		2010