Recent work on dynamic interventions has greatly expanded the range of causal questions that researchers can study. Simultaneously, this work has weakened identifying assumptions, yielding effects that are more practically relevant. Most work in dynamic interventions to date has focused on settings where we directly alter some unconfounded treatment of interest. In policy analysis, decision makers rarely have this level of control over behaviours or access to experimental data. Instead, they are often faced with treatments that they can affect only indirectly and whose effects must be learned from observational data. We propose new estimands and estimators of causal effects based on dynamic interventions with instrumental variables. This method does not rely on parametric models and does not require an experiment. Instead, we estimate the effect of treatment induced by a dynamic intervention on an instrument. This robustness should reassure policy makers that these estimates can be used to inform policy effectively. We demonstrate the usefulness of this estimation strategy in a case‐study examining the effect of visitation on recidivism.

中文翻译：

---

使用动态干预的工具变量方法

关于动态干预的最新工作大大扩展了研究人员可以研究的因果问题的范围。同时，这项工作削弱了确定性假设，产生了与实际更相关的影响。迄今为止，动态干预中的大多数工作都集中在我们直接更改一些无混淆的感兴趣治疗的设置上。在政策分析中，决策者很少对行为或访问实验数据具有这种级别的控制。相反，他们经常面临只能间接影响的治疗，必须从观察数据中了解其效果。我们基于工具变量的动态干预提出新的估计和因果效应估计。该方法不依赖参数模型，不需要实验。代替，我们估计了动态干预对器械产生的治疗效果。这种鲁棒性应使决策者放心，这些估计可用于有效地为政策提供信息。我们在个案研究中考察了探视对累犯的影响，证明了这种估计策略的有用性。