Estimation of Treatment Effects Under Nonstationarity via Truncated Difference-in-Q’s

This academic paper introduces a novel truncated Difference-in-Q’s (DQ) estimator designed for A/B testing in dynamic, nonstationary environments. Unlike traditional methods that struggle with temporal interference and changing system dynamics, this estimator effectively measures the global average treatment effect (GATE) by considering truncated outcome trajectories. The authors theoretically demonstrate that their approach offers reduced bias and variance compared to existing estimators, particularly in scenarios where conditions are not constant over time. Empirical validations using simulated emergency department and ride-sharing systems further confirm the estimator's practical utility and robustness in real-world, fluctuating settings. The research highlights the estimator's ease of implementation and its independence from full state observability, making it a valuable tool for practitioners.