2007 Experimentation Measurement Optimization 
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., 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.
Occam's Razor
SEPTEMBER 19, 2011
We have to do Search Engine Optimization. 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!!!),
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
The Unofficial Google Data Science Blog
JULY 22, 2020
Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.
The Unofficial Google Data Science Blog
OCTOBER 7, 2015
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. Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning.
Occam's Razor
AUGUST 22, 2011
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?
Occam's Razor
APRIL 8, 2013
Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. 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. Measure and decide what to do.
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. It should be noted that inverse probability weighting is not generally optimal (i.e., This is often referred to as the positivity assumption. the curse of dimensionality).
Expert insights. Personalized for you.
52 
Let's personalize your content