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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.
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.
Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals
Recent studies have shown that information disclosed on social network sites (such as Facebook) can be used to predict personal characteristics with surprisingly high accuracy.
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