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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Crucially, it takes into account the uncertainty inherent in our experiments. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. From a Bayesian perspective, one can combine joint posterior samples for $E[Y_i | T_i=t, E_i=j]$ and $P(E_i=j)$, which provides a measure of uncertainty around the estimate. ACM, 2017. [4]