D-Optimal Design for Network A/B Testing

A/B testing refers to the statistical procedure of experimental design and analysis to compare two treatments, A and B, applied to different testing subjects. It is widely used by technology companies such as Facebook, LinkedIn, and Netflix, to compare different algorithms, web-designs, and other on...

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Vydáno v:Journal of statistical theory and practice Ročník 13; číslo 4
Hlavní autoři: Pokhilko, Victoria, Zhang, Qiong, Kang, Lulu, Mays, D’arcy P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.12.2019
Témata:
Algorithms
Analysis and Advanced Methodologies in the Design of Experiments
Mathematics and Statistics
Original Article
Probability Theory and Stochastic Processes
Statistical Theory and Methods
Statistics
D-optimal design
Conditional auto-regressive model
Mixed integer programming
Social network
A/B testing
ISSN:1559-8608, 1559-8616
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Popis
Shrnutí:A/B testing refers to the statistical procedure of experimental design and analysis to compare two treatments, A and B, applied to different testing subjects. It is widely used by technology companies such as Facebook, LinkedIn, and Netflix, to compare different algorithms, web-designs, and other online products and services. The subjects participating in these online A/B testing experiments are users who are connected in different scales of social networks. Two connected subjects are similar in terms of their social behaviors, education and financial background, and other demographic aspects. Hence, it is only natural to assume that their reactions to online products and services are related to their network adjacency. In this paper, we propose to use the conditional auto-regressive model to present the network structure and include the network effects in the estimation and inference of the treatment effect. A D-optimal design criterion is developed based on the proposed model. Mixed integer programming formulations are developed to obtain the D-optimal designs. The effectiveness of the proposed method is shown through numerical results with synthetic networks and real social networks.
ISSN:1559-8608
1559-8616
DOI:10.1007/s42519-019-0058-3