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The problem of measuring the true incremental effectiveness of a digitaladvertising campaign is of increasing importance to marketers. With a large andincreasing percentage of digital advertising delivered viaDemand-Side-Platforms (DSPs) executing campaigns via Real-Time-Bidding (RTB)auctions and programmatic approaches, a measurement solution that satisfiesboth advertiser concerns and the constraints of a DSP is of particularinterest.  MediaMath (a DSP) has developed the first practical, statistically soundrandomization-based methodology for causal ad effectiveness (or Ad Lift)measurement by a DSP (or similar digital advertising execution system that maynot have full control over the advertising transaction mechanisms). We describeour solution and establish its soundness within the causal framework ofcounterfactuals and potential outcomes, and present a Gibbs-sampling procedurefor estimating confidence intervals around the estimated Ad Lift. We alsoaddress practical complications (unique to the digital advertising setting)that stem from the fact that digital advertising is targeted and measured viaidentifiers (e.g., cookies, mobile advertising IDs) that may not be stable overtime. One such complication is the repeated occurrence of identifiers, leadingto interference among observations. Another is due to the possibility ofmultiple identifiers being associated with the same consumer, leading to"contamination" with some of their identifiers being assigned to the Treatmentgroup and others to the Control group. Complications such as these haveseverely impaired previous efforts to derive accurate measurements of lift inpractice.  In contrast to a few other papers on the subject, this paper has anexpository aim as well, and provides a rigorous, self-contained, andreadily-implementable treatment of all relevant concepts.

Counterfactual-based Incrementality Measurement in a Digital Ad-Buying Platform