This paper develops a novel nonparametric identification method for treatment effects in settings where individuals self-select into treatment sequences. I propose an identification strategy which relies on a dynamic version of standard Instrumental Variables (IV) assumptions and builds on a dynamic version of the Marginal Treatment Effects (MTE) as the fundamental building block for treatment effects. The main contribution of the paper is to relax assumptions on the support of the observed variables and on unobservable gains of treatment that are present in the dynamic treatment effects literature. Monte Carlo simulation studies illustrate the desirable finite-sample performance of a sieve estimator for MTEs and Average Treatment Effects (ATEs) on a close-to-application simulation study.

翻译：本文针对个体自主选择治疗序列的场景，提出了一种新型非参数治疗效应识别方法。本识别策略基于标准工具变量（IV）假设的动态化版本，并以动态边际治疗效应（MTE）作为治疗效应的核心构成要素。本文的主要贡献在于放宽了动态治疗效应文献中关于观测变量支撑集及治疗不可观测收益的假设条件。通过蒙特卡洛模拟研究，本文在贴近实际应用的模拟场景中验证了筛分估计量在有限样本条件下对MTE与平均治疗效应（ATE）的理想估计性能。