Randomized controlled trials, commonly known as A/B tests, are the standard mechanism for inferring causal effects of product innovations. Standard platforms evaluate these tests with fixed-horizon procedures that control the false positive rate at a predetermined time, forcing teams to wait until the end even when early data strongly indicates success or failure. Unfortunately, continuous monitoring ("peeking") invalidates these guarantees, while always-valid inference is overly conservative under a strict deadline, and group sequential designs (GSD) have received limited traction in large-scale experimentation due to operational barriers: terminal-rule preservation, autocorrelation, latent effect-size distributions, calendar-duration optimization. We fill in this gap by proposing a Predictive Probability of Success (PPoS) group sequential framework that is autocorrelation-aware and duration-optimal while preserving the platform's fixed-horizon terminal rule. Our key insight is that PPoS thresholds map to closed-form lift boundaries that converge to the standard test at the horizon by construction, admit an effective-sample-size correction estimated from historical A/A tests, and suffice to attain the likelihood-ratio efficiency floor. Experimentally on online A/A and A/B experiments, we show that the proposed policy reduces expected duration by 43.1% (7.97 days vs. 14-day baseline), increasing effective platform capacity by 76%, while retaining the guarantees on FPR and power.

翻译：暂无翻译