Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework. (arXiv:2002.01711v5 [cs.LG] UPDATED)

A/B testing, or online experiment is a standard business strategy to compare
a new product with an old one in pharmaceutical, technological, and traditional
industries. Major challenges arise in online experiments of two-sided
marketplace platforms (e.g., Uber) where there is only one unit that receives a
sequence of treatments over time. In those experiments, the treatment at a
given time impacts current outcome as well as future outcomes. The aim of this
paper is to introduce a reinforcement learning framework …

a/b testing arxiv b testing framework learning reinforcement learning testing