It’s easy to make experimental design mistakes that invalidate your online controlled experiments. At an organisation like Facebook (who kindly supplied the corpus of experiments used in this study), the state of art is to have a pool of experts carefully review all experiments. PlanAlyzer acts a bit like a linter for online experiment designs, where those designs are specified in the PlanOut language.
As well as pointing out any bugs in the experiment design, PlanAlyzer will also output a set of contrasts — comparisons that you can safely make given the design of the experiment. Hopefully the comparison you wanted to make when you set up the experiment is in that set!
Regular readers of The Morning Paper will be well aware that there’s plenty that can go wrong in the design and interpretation of online controlled experiments (see e.g. ‘A dirty dozen: twelve common metric interpretation pitfalls in online controlled experiments’) . PlanAnalyzer is aimed at detecting threats to internal validity, the degree to which valid causal conclusions can (or cannot!) be drawn from a study.
This is a linkpost for https://blog.acolyer.org/2019/11/22/planalyzer/