Estimation of Treatment Effects Under Nonstationarity via the Truncated Policy Gradient Estimator
利用截断策略梯度估计量估计非平稳环境中的治疗效应
Randomized experiments (or A/B tests) are widely used to evaluate interventions in dynamic systems such as recommendation platforms, marketplaces, and digital health. In these settings, interventions affect both current and future system states, so estimating the global average treatment effect (GATE) requires accounting for temporal dynamics, which is especially challenging in the presence of nonstationarity; existing approaches suffer from high bias, high variance, or…