Have it both ways : from A/B testing to A&B testing with exceptional model mining

In traditional A/B testing, we have two variants of the same product, a pool of test subjects, and a measure of success. In a randomized experiment, each test subject is presented with one of the two variants, and the measure of success is aggregated per variant. The variant of the product associated with the most success is retained, while the other variant is discarded. This, however, presumes that the company producing the products only has enough capacity to maintain one of the two product variants. If more capacity is available, then advanced data science techniques can extract more profit for the company from the A/B testing results. Exceptional Model Mining is one such advanced data science technique, which specializes in identifying subgroups that behave differently from the overall population. Using the association model class for EMM, we can find subpopulations that prefer variant A where the general population prefers variant B, and vice versa. This data science technique is applied on data from StudyPortals, a global study choice platform that ran an A/B test on the design of aspects of their website

The Future of A/B Testing in Social Network Advertising

This research addresses the future of A/B testing in social network advertising. A/B test is a well- studied comparison problem with two different samples with the goal of testing the treatment effect of old and new variations. In recent years, through the rise of the internet, A/B testing in social networks has gained sharpened focus and is commonly used in social network advertising. Due to the market-driven strategy the companies should today aim for, the development of A/B testing in social network advertising can help in gathering useful insights of consumer preferences and attitudes. A/B testing has been perceived as cheap, simple and reliable way of optimizing advertisement and mining data from site users. However, as currently performed A/B testing has criticized as manual and time-consuming activity that requires complex set of statistical and engineering skills. This study focuses on overcoming these problems through automation and machine learning algorithms. Besides, the importance of shifting organizational focus on optimal usage of data-driven decision making through A/B testing, and user attitudes towards social network advertising and their ad-clicking behaviour are addressed

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested