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.
Smart Data Collective
FEBRUARY 4, 2020
In a report on the failure rates of drug discovery efforts between 2013 and 2015, Richard K. An open and impartial AI model should be able to inject a measure of transparency into this process along with the obvious efficiency advantages. Unfortunately, a substantial number of clinical trials fails in these two Phases.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
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.
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.
Occam's Razor
JANUARY 9, 2012
Most companies are astonishingly blasé about data and possibilities of measurement. " Sad, unimaginative measurements of their sad, unimaginative campaigns. Allocate some of your aforementioned 15% budget to experimentation and testing. This blog is about the joys of measurement and the transformative power of data.
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. See Hainmueller (2012), and the work of Zhao & Percival (2015) for more details on how this optimization problem is solved, and for further discussion. R package version 2.0-15.
Domino Data Lab
FEBRUARY 4, 2019
Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. The ability to measure results (risk-reducing evidence). Ensure a culture that supports a steady process of learning and experimentation.
Expert insights. Personalized for you.
52 
Let's personalize your content