Remove 2007 Remove Experimentation Remove Measurement Remove Statistics
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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., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

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Measuring Incrementality: Controlled Experiments to the Rescue!

Occam's Razor

This: You understand all the environmental variables currently in play, you carefully choose more than one group of "like type" subjects, you expose them to a different mix of media, measure differences in outcomes, prove / disprove your hypothesis (DO FACEBOOK NOW!!!), Measuring Incrementality: Controlled Experiments to the Rescue!

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Knowledge

Occam's Razor

Key To Your Digital Success: Web Analytics Measurement Model. " Measuring Incrementality: Controlled Experiments to the Rescue! Barriers To An Effective Web Measurement Strategy [+ Solutions!]. Measuring Online Engagement: What Role Does Web Analytics Play? "Engagement" How Do I Measure Success?

KPI 124
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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do. For each of them, write down the KPI you're measuring, and what that KPI should be for you to consider your efforts a success. Measure and decide what to do.

Metrics 156
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Estimating causal effects using geo experiments

The Unofficial Google Data Science Blog

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).