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Measuring the causal effect of advertising on driving desired behavior is an important problem to the digital publishing industry (the "attribution" problem). It is common to use observational methods for attribution, due to the high cost and difficulty of employing randomized controlled trials (RCTs). However, recent results have shown that even current sophisticated observational methods may be inaccurate, yielding incorrect estimates of the true effect of advertising. Here, we present a new observational attribution method based on a successful model of neural spiking that learns the temporal interactions between event-based time series. We train this model on data from several RCT marketing experiments, and show that it can accurately recover the true causal attribution.

Attribution Inference for Digital Advertising using Inhomogeneous Poisson Models