The proximal causal inference framework enables the identification and estimation of causal effects in the presence of unmeasured confounding by leveraging two disjoint sets of observed strong proxies: negative control treatments and negative control outcomes. In the point exposure setting, this framework has primarily been applied to estimands comparing counterfactual outcomes under a static fixed intervention or, possibly randomized, regime that depends on baseline covariates. For continuous exposures, alternative hypothetical scenarios can enrich our understanding of causal effects, such as those where each individual receives their observed treatment dose modified in a pre-specified manner - commonly referred to as modified treatment regimes. In this work, we extend the proximal causal inference framework to identify and estimate the mean outcome under a modified treatment regime, addressing this gap in the literature. We propose a flexible strategy that does not rely on the assumption that all confounders have been measured - unlike existing estimators - and leverages modern debiased machine learning techniques using non-parametric estimators of nuisance functions to avoid restrictive parametric assumptions. Our methodology was motivated by immunobridging studies of COVID-19 vaccines aimed at identifying correlates of protection, where the individual's underlying immune capacity is an important unmeasured confounder. We demonstrate its applicability using data from such a study and evaluate its finite-sample performance through simulation studies.

翻译：近端因果推断框架通过利用两组互不相交的观测强代理变量——负控制治疗与负控制结局，能够在存在未测量混杂因素的情况下识别和估计因果效应。在点暴露设定中，该框架主要应用于比较静态固定干预（或可能依赖于基线协变量的随机化方案）下反事实结局的估计量。对于连续暴露，替代性假设情景可以丰富我们对因果效应的理解，例如每个个体接受按预先指定方式修正的观测治疗剂量的情形——通常称为修正治疗策略。本研究将扩展近端因果推断框架，以识别和估计修正治疗策略下的平均结局，填补文献中的这一空白。我们提出一种灵活的策略，该策略不依赖于所有混杂因素均已测量的假设（与现有估计量不同），并利用基于非参数估计的现代去偏机器学习技术来避免限制性参数假设。我们的方法受到COVID-19疫苗免疫桥接研究的启发，该研究旨在识别保护性相关因素，其中个体基础免疫能力是重要的未测量混杂因素。我们通过此类研究的数据展示了该方法的适用性，并通过模拟研究评估了其有限样本性能。