[HTML][HTML] A/B testing: A systematic literature review

F Quin, D Weyns, M Galster, CC Silva - Journal of Systems and Software, 2024 - Elsevier
A/B testing, also referred to as online controlled experimentation or continuous
experimentation, is a form of hypothesis testing where two variants of a piece of software are …

[图书][B] Trustworthy online controlled experiments: A practical guide to a/b testing

R Kohavi, D Tang, Y Xu - 2020 - books.google.com
Getting numbers is easy; getting numbers you can trust is hard. This practical guide by
experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate …

[DOC][DOC] Online experimentation: Benefits, operational and methodological challenges, and scaling guide

I Bojinov, S Gupta - Harvard Data Science Review, 2022 - assets.pubpub.org
In the past decade, online controlled experimentation, or A/B testing, at scale has proved to
be a significant driver of business innovation. The practice was first pioneered by the …

Online randomized controlled experiments at scale: lessons and extensions to medicine

R Kohavi, D Tang, Y Xu, LG Hemkens, JPA Ioannidis - Trials, 2020 - Springer
Background Many technology companies, including Airbnb, Amazon, Booking. com, eBay,
Facebook, Google, LinkedIn, Lyft, Microsoft, Netflix, Twitter, Uber, and Yahoo!/Oath, run …

Anytime-valid inference for multinomial count data

M Lindon, A Malek - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Many experiments compare count outcomes among treatment groups. Examples include the
number of successful signups in conversion rate experiments or the number of errors …

Using survival models to estimate user engagement in online experiments

P Chandar, B St. Thomas, L Maystre, V Pappu… - Proceedings of the …, 2022 - dl.acm.org
Online controlled experiments, in which different variants of a product are compared based
on an Overall Evaluation Criterion (OEC), have emerged as a gold standard for decision …

Variance Reduction Using In-Experiment Data: Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes

A Deng, M Du, A Matlin, Q Zhang - … of the 29th ACM SIGKDD Conference …, 2023 - dl.acm.org
Improving statistical power is a common challenge for online experimentation platforms so
that more hypotheses can be tested and lower effect sizes can be detected. To increase the …

Balancing approach for causal inference at scale

S Lin, M Xu, X Zhang, SK Chao, YK Huang… - Proceedings of the 29th …, 2023 - dl.acm.org
With the modern software and online platforms to collect massive amount of data, there is an
increasing demand of applying causal inference methods at large scale when randomized …

Novelty and primacy: a long-term estimator for online experiments

S Sadeghi, S Gupta, S Gramatovici, J Lu, H Ai… - …, 2022 - Taylor & Francis
Online experiments are the gold standard for evaluating impact on user experience and
accelerating innovation in software. However, since experiments are typically limited in …

A/B Integrations: 7 Lessons Learned from Enabling A/B testing as a Product Feature

A Fabijan, P Dmitriev, B Arai, A Drake… - 2023 IEEE/ACM 45th …, 2023 - ieeexplore.ieee.org
A/B tests are the gold standard for evaluating product changes. At Microsoft, for example, we
run tens of thousands of A/B tests every year to understand how users respond to new …