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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. A/B testing is used widely in information technology companies to guide product development and improvements.

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Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

Posteriors are useful to understand the system, measure accuracy, and make better decisions. Methods like the Poisson bootstrap can help us measure the variability of $t$, but don’t give us posteriors either, particularly since good high-dimensional estimators aren’t unbiased. Figure 4 shows the results of such a test.

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