2012 Experimentation Measurement Metrics 
The Unofficial Google Data Science Blog
APRIL 23, 2024
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., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.
The Unofficial Google Data Science Blog
JUNE 30, 2016
In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. In fact, Hainmueller (2012) show that entropy balancing is equivalent to estimating the weights as a log-linear model of the covariate functions $c_j(X)$.
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The Unofficial Google Data Science Blog
MAY 31, 2016
It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. Let's take a look at larger groups of individuals whose aggregate behavior we can measure. days or weeks).
The Unofficial Google Data Science Blog
OCTOBER 12, 2017
With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. We data scientists now have access to tools that allow us to run a large numbers of experiments, and then to slice experimental populations by any combination of dimensions collected.
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