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Predictive Incrementality by Experimentation (PIE) for Ad Measurement
We present a novel approach to causal measurement for advertising, namely to use exogenous variation in advertising exposure (RCTs) for a subset of ad campaigns to build a model that can predict the causal effect of ad campaigns that were run without RCTs.
Multicell experiments for marginal treatment effect estimation of digital ads
Randomized experiments with treatment and control groups are an important tool to measure the impacts of interventions.
Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement
Although DML performs better than SPSM, neither method performs well, even using flexible deep learning models to implement the propensity and outcome models.
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