论文标题
自适应实验中的自适应双重估计器，以进行自适应实验和有关记录政策的悖论
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy
论文作者
论文摘要
双重稳健（DR）估计器由两个滋扰参数组成，条件平均结果和记录策略（选择动作的概率）在因果推断中至关重要。本文提出了DR估计量，用于从自适应实验中获得的依赖样品。为了从具有非donsker滋扰估计量的依赖样品中获得渐近正常的半参数估计器，我们提出自适应拟合作为样品分解的变体。我们还报告了一个经验悖论，我们提议的DR估算器倾向于与使用真实记录策略的其他估计器相比，表现出更好的性能。虽然类似的现象以I.I.D的估计器而闻名。样本，基于渐近效率的传统解释不能用依赖的样本阐明我们的情况。我们通过模拟研究证实了这一假设。
The doubly robust (DR) estimator, which consists of two nuisance parameters, the conditional mean outcome and the logging policy (the probability of choosing an action), is crucial in causal inference. This paper proposes a DR estimator for dependent samples obtained from adaptive experiments. To obtain an asymptotically normal semiparametric estimator from dependent samples with non-Donsker nuisance estimators, we propose adaptive-fitting as a variant of sample-splitting. We also report an empirical paradox that our proposed DR estimator tends to show better performances compared to other estimators utilizing the true logging policy. While a similar phenomenon is known for estimators with i.i.d. samples, traditional explanations based on asymptotic efficiency cannot elucidate our case with dependent samples. We confirm this hypothesis through simulation studies.
