  A/B tests serve the purpose of reliably identifying the effect of changes
introduced in online services. It is common for online platforms to run a large
number of simultaneous experiments by splitting incoming user traffic randomly
in treatment and control groups. Despite a perfect randomization between
different groups, simultaneous experiments can interact with each other and
create a negative impact on average population outcomes such as engagement
metrics. These are measured globally and monitored to protect overall user
experience. Therefore, it is crucial to measure these interaction effects and
attribute their overall impact in a fair way to the respective experimenters.
We suggest an approach to measure and disentangle the effect of simultaneous
experiments by providing a cost sharing approach based on Shapley values. We
also provide a counterfactual perspective, that predicts shared impact based on
conditional average treatment effects making use of causal inference
techniques. We illustrate our approach in real world and synthetic data
experiments.
