Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement – MSI

Working Paper

Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement

Brett R. Gordon

Northwestern University

Florian Zettelmeyer

Northwestern University

Robert Moakler

Mar 28, 2022

Applies two new machine learning techniques to analyze observational data as alternatives to Randomized Controlled Trials (RCTs) to estimate the causal effects of advertising.

Topics

research recap

Can Observational Methods Assess the Causal Effect of Ads?

Randomized Controlled Trials (RCTs) are not always feasible for advertising measurement. Even using newer machine learning techniques, alternative observational methods are unlikely to reliably estimate causal effects until advertising platforms are willing to provide more information about how they implement auctions and deliver targeted ads.

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