Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an assumption that is fundamentally untestable with observed data. To this end, this paper proposes a method for sensitivity analysis of the ITE, a way to estimate a range of the ITE under unobserved confounding. The method we develop quantifies unmeasured confounding through a marginal sensitivity model [Ros2002, Tan2006], and then adapts the framework of conformal inference to estimate an ITE interval at a given confounding strength. In particular, we formulate this sensitivity analysis problem as one of conformal inference under distribution shift, and we extend existing methods of covariate-shifted conformal inference to this more general setting. The result is a predictive interval that has guaranteed nominal coverage of the ITE, a method that provides coverage with distribution-free and nonasymptotic guarantees. We evaluate the method on synthetic data and illustrate its application in an observational study.
翻译:估计个人治疗效果(ITE)对于个人化决策至关重要。然而,估算ITE的现有方法往往依赖缺乏根据的假设,这种假设从根本上说无法用观察到的数据检验。为此,本文件提出对ITE进行敏感度分析的方法,一种在没有观察到的混乱情况下估计一系列ITE的方法。我们制定的方法通过一种边际敏感度模型[Ros2002, Tan2006]量化了非计量混杂,然后调整了在某种混凝固的强度下估计ITE间隔的一致推论框架。我们特别将这一敏感度分析问题作为分配变化中一致推论的一种,并将现有的千变同式相推论方法扩大到这一更为笼统的场景。结果是一种预测间隔,保证了ITE的名义涵盖范围[Ros2002, Tan2006], 并随后调整了对合成数据方法进行评估并在一项观察研究中说明其应用情况。