Modified treatment policies are a widely applicable class of interventions used to study the causal effects of continuous exposures. Approaches to evaluating their causal effects assume no interference, meaning that such effects cannot be learned from data in settings where the exposure of one unit affects the outcome of others, as is common in spatial or network data. We introduce a new class of intervention, induced modified treatment policies, which we show identify such causal effects in the presence of network interference. Building on recent developments in network causal inference, we provide flexible, semi-parametric efficient estimators of the identified statistical estimand. Simulation experiments demonstrate that an induced modified treatment policy can eliminate causal (or identification) bias resulting from interference. We use the methods developed to evaluate the effect of zero-emission vehicle uptake on air pollution in California, strengthening prior evidence.

翻译：修正治疗策略是研究连续暴露因果效应的一类广泛适用的干预方法。现有评估其因果效应的方法均假设不存在干扰，这意味着当某一单元的暴露会影响其他单元的结果时（如空间或网络数据中常见的情形），此类效应无法从数据中学习。我们提出了一类新的干预方法——诱导修正治疗策略，并证明该方法能够在网络干扰存在的情况下识别此类因果效应。基于网络因果推断的最新进展，我们为所识别的统计估计量提供了灵活的半参数高效估计器。模拟实验表明，诱导修正治疗策略能够消除由干扰导致的因果（或识别）偏差。我们运用所开发的方法评估了加州零排放车辆普及对空气污染的影响，强化了先前的证据。