用平均导数效应参数化连续处理的影响
Parameterising the effect of a continuous treatment using average derivative effects
AI总结：
本文提出了一种基于平均导数效应的参数化方法，用于连续处理影响的估计，通过统一视角扩展了最优加权结果到连续处理场景，并开发了高效的机器学习估计器。
摘要：
平均处理效应（ATE）通常用于量化二元处理对结果的主要影响。对连续处理的扩展通常基于剂量-反应曲线或移位干预，但两者都需要强烈的重叠条件，且所得到的曲线可能难以总结。我们转而关注平均导数效应（ADEs），这些是与微小移位干预相关的标量估计量，仅需局部重叠假设。然而，ADEs在实践中很少被使用，因为其估计通常需要估计条件密度函数。通过表征加权ADEs的Riesz代表元，我们提出了一类新的估计量，提供了连续/二元处理情况下加权ADEs/ATEs的统一视角。我们推导了我们类中最小化非参数效率下限的估计量，从而将二元处理文献中的最优加权结果扩展到连续设置。我们为两种加权ADEs开发了高效的估计器，避免了密度估计，并适用于现代机器学习方法，我们在模拟和华法林剂量效应的应用分析中进行了评估。
英文摘要：
The average treatment effect (ATE) is commonly used to quantify the main effect of a binary treatment on an outcome. Extensions to continuous treatments are usually based on the dose-response curve or shift interventions, but both require strong overlap conditions and the resulting curves may be difficult to summarise. We focus instead on average derivative effects (ADEs) that are scalar estimands related to infinitesimal shift interventions requiring only local overlap assumptions. ADEs, however, are rarely used in practice because their estimation usually requires estimating conditional density functions. By characterising the Riesz representers of weighted ADEs, we propose a new class of estimands that provides a unified view of weighted ADEs/ATEs when the treatment is continuous/binary. We derive the estimand in our class that minimises the nonparametric efficiency bound, thereby extending optimal weighting results from the binary treatment literature to the continuous setting. We develop efficient estimators for two weighted ADEs that avoid density estimation and are amenable to modern machine learning methods, which we evaluate in simulations and an applied analysis of Warfarin dosage effects.
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