The causal effects of continuous treatments are often characterized through the average dose response function, which is challenging to estimate from observational data due to confounding and positivity violations. Modified treatment policies (MTPs) are an alternative approach that aim to assess the effect of a modification to observed treatment values and work under relaxed assumptions. Estimators for MTPs generally focus on estimating the conditional density of treatment given covariates and using it to construct weights. However, weighting using conditional density models has well-documented challenges. Further, MTPs with larger treatment modifications have stronger confounding and no tools exist to help choose an appropriate modification magnitude. This paper investigates the role of weights for MTPs showing that to control confounding, weights should balance the weighted data to an unobserved hypothetical target population that can be characterized with observed data. Leveraging this insight, we present a versatile set of tools to enhance estimation for MTPs. We introduce a distance that measures imbalance of covariate distributions under the MTP and use it to develop new weighting methods and tools to aid in the estimation of MTPs. Using our methods we study the effect of mechanical power of ventilation on in-hospital mortality.

翻译：连续治疗的因果效应通常通过平均剂量响应函数来表征，但由于混杂因素和正性假设的违背，从观测数据中估计该函数具有挑战性。修正治疗策略（MTPs）是一种替代方法，旨在评估对观测治疗值的修正所产生的效应，并在更宽松的假设条件下适用。MTPs的估计器通常侧重于估计给定协变量下的治疗条件密度，并利用该密度构建权重。然而，使用条件密度模型进行加权存在公认的挑战。此外，治疗修正幅度较大的MTPs具有更强的混杂性，目前尚无工具可用于帮助选择合适的修正幅度。本文研究了权重在MTPs中的作用，表明为控制混杂因素，权重应使加权数据平衡至一个可通过观测数据表征的未观测假设目标总体。基于这一见解，我们提出了一套多功能工具以改进MTPs的估计。我们引入了一种度量MTP下协变量分布不平衡性的距离，并利用该距离开发了新的加权方法和辅助MTP估计的工具。应用我们的方法，我们研究了通气机械功率对院内死亡率的影响。