The 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. We illustrate our methods through an example studying the effect of mechanical power of ventilation on in-hospital mortality.

翻译：连续治疗的效果通常通过平均剂量反应函数来刻画，但由于混杂和阳性违反，从观察数据中估计该函数具有挑战性。改良治疗策略（MTPs）是一种替代方法，旨在评估对观察到的治疗值进行修改后的效果，并在宽松假设下运行。MTP的估计量通常侧重于估计给定协变量下治疗的条件密度，并利用其构建权重。然而，使用条件密度模型进行加权存在公认的困难。此外，较大治疗修改幅度的MTP会引入更强的混杂，且目前尚无工具可帮助选择合适的修改幅度。本文研究了权重在MTP中的作用，表明为控制混杂，权重应使加权数据平衡到可通过观察数据表征的未观察到的假设目标人群。基于这一见解，我们提出了一套多功能工具来增强MTP的估计。我们引入了一种距离度量，用于衡量MTP下协变量分布的不平衡性，并据此开发了新的加权方法和辅助MTP估计的工具。我们通过一个研究机械通气功率对住院死亡率影响的示例说明了这些方法。