  The Randomized Controlled Trial (RCT) or A/B testing is considered the gold
standard method for estimating causal effects. Fisher famously advocated
randomly allocating experiment units into treatment and control groups to
preclude systematic biases. We propose a variant of systematic sampling called
Covariate Ordered Systematic Sampling (COSS). In COSS, we order experimental
units using a pre-experiment covariate and allocate them alternately into
treatment and control groups. Using theoretical proofs, experiments on
simulated data, and hundreds of A/B tests conducted within 3 real-world
marketing campaigns, we show how our method achieves better sensitivity gains
than commonly used variance reduction techniques like CUPED while retaining the
simplicity of RCTs.
