Optogenetics is widely used to study the effects of neural circuit manipulation on behavior. However, the paucity of causal inference methodological work on this topic has resulted in analysis conventions that discard information, and constrain the scientific questions that can be posed. To fill this gap, we introduce a nonparametric causal inference framework for analyzing "closed-loop" designs, which use dynamic policies that assign treatment based on covariates. In this setting, standard methods can introduce bias and occlude causal effects. Building on the sequentially randomized experiments literature in causal inference, our approach extends history-restricted marginal structural models for dynamic regimes. In practice, our framework can identify a wide range of causal effects of optogenetics on trial-by-trial behavior, such as, fast/slow-acting, dose-response, additive/antagonistic, and floor/ceiling. Importantly, it does so without requiring negative controls, and can estimate how causal effect magnitudes evolve across time points. From another view, our work extends "excursion effect" methods--popular in the mobile health literature--to enable estimation of causal contrasts for treatment sequences greater than length one, in the presence of positivity violations. We derive rigorous statistical guarantees, enabling hypothesis testing of these causal effects. We demonstrate our approach on data from a recent study of dopaminergic activity on learning, and show how our method reveals relevant effects obscured in standard analyses.

翻译：光遗传学被广泛应用于研究神经回路操控对行为的影响。然而，该领域因果推断方法学研究的匮乏导致现有分析惯例常丢弃信息，并限制了可提出的科学问题。为填补这一空白，我们引入了一个非参数因果推断框架，用于分析“闭环”实验设计——这类设计采用基于协变量分配干预的动态策略。在此设定下，标准方法可能引入偏差并掩盖因果效应。基于因果推断中的序贯随机化实验文献，我们的方法扩展了适用于动态干预机制的历史限制边际结构模型。在实践中，该框架能够识别光遗传学对逐试次行为的多类因果效应，例如快/慢效性、剂量反应、加性/拮抗性及底限/上限效应。重要的是，该方法无需阴性对照即可实现上述功能，并能估计因果效应强度随时间点的演化过程。从另一视角看，本研究扩展了移动健康领域中流行的“偏离效应”方法，使其在存在正性违反的情况下，能够估计长度大于一的治疗序列的因果对比量。我们推导了严格的统计保证，从而支持对这些因果效应进行假设检验。我们在近期一项关于多巴胺能活动对学习影响的研究数据上验证了本方法，并展示了该方法如何揭示被标准分析所掩盖的相关效应。