This paper proposes a confidence interval construction for heterogeneous treatment effects in the context of multi-stage experiments with $N$ samples and high-dimensional, $d$, confounders. Our focus is on the case of $d\gg N$, but the results obtained also apply to low-dimensional cases. We showcase that the bias of regularized estimation, unavoidable in high-dimensional covariate spaces, is mitigated with a simple double-robust score. In this way, no additional bias removal is necessary, and we obtain root-$N$ inference results while allowing multi-stage interdependency of the treatments and covariates. Memoryless property is also not assumed; treatment can possibly depend on all previous treatment assignments and all previous multi-stage confounders. Our results rely on certain sparsity assumptions of the underlying dependencies. We discover new product rate conditions necessary for robust inference with dynamic treatments.

翻译：本文建议,在以美元样本和高度样本和高度样本进行多阶段实验的情况下,为不同治疗效果构建一个信任区间结构,我们的重点是美元,我们的重点是美元,但所获得的结果也适用于低度案例。我们展示的是,在高维共变空间中不可避免的正规估计的偏差会以简单的双压评分来减轻。这样,就没有必要再消除偏差,我们获得根值-美元推论结果,同时允许处理和共变的多级相互依存关系。没有记忆的属性也不会被假定;治疗可能取决于以前的所有治疗任务和以往所有多阶段的共变体。我们的结果取决于基本依赖性的某些宽度假设。我们发现了以动态处理进行稳健的推论所必要的新产品率条件。