Under ordinary conditions, carrying out an experiment gives the researchers an unbiased estimate of their parameter of interest.
Note that benchmarking is an attempt to calibrate non-statistical uncertainty (flaws in underlying assumptions).
[4] Non-experimental, or observational, research designs compare treated to untreated subjects while controlling for background attributes (called covariates).
They find that despite great variation in variables within their data, observational methods cannot recover the causal effects of online advertising.
This study ultimately provides evidence that without a randomized control trial, it is impossible to detect symptoms of bias.
[7] Glazerman, Levy and Myers (2003) perform experimental benchmarking in the context of employment services, welfare and job training.
Specifically, the study aims to analyze the effectiveness of Facebook ads on three outcomes: checkout, registration and page view.
They find that despite great variation made possible by the nature of social media, it is not possible to accurately recover the causal effects.