Conformal Sensitivity Analysis for Individual Treatment Effects
Conformal Sensitivity Analysis for Individual Treatment Effects
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DOI：
10.1080/01621459.2022.2102503
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发表时间：
2021-12
影响因子：
3.7
通讯作者：
Mingzhang Yin;Claudia Shi;Yixin Wang;D. Blei
中科院分区：
文献类型：
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作者：
Mingzhang Yin;Claudia Shi;Yixin Wang;D. Blei
摘要评估个体化治疗效果(ITE)是个体化决策的关键。然而，现有的估计ITE的方法通常依赖于无混乱性，这一假设从根本上无法用观测数据进行检验。为了评估个体水平无混杂因果结论的稳健性，本文提出了一种ITE的灵敏度分析方法，即在不可观测的混杂情况下估计ITE的范围。我们开发的方法通过边际敏感性模型来量化未测量的混杂，并采用保角推理的框架来估计给定混杂强度下的ITE区间。特别地，我们将这种灵敏度分析描述为分布平移下的共形推断问题，并将已有的协变量平移共形推断方法推广到这种更一般的情形。由此产生的预测区间保证了ITE的名义覆盖率，并为该覆盖率提供了无分布和非渐近保证。我们在合成数据上对该方法进行了评估，并说明了它在观测研究中的应用。这篇文章的补充材料可以在网上找到。
Abstract 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 assess the robustness of individual-level causal conclusion with unconfoundedness, this article 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, and adapts the framework of conformal inference to estimate an ITE interval at a given confounding strength. In particular, we formulate this sensitivity analysis as a conformal inference problem under distribution shift, and we extend existing methods of covariate-shifted conformal inference to this more general setting. The resulting predictive interval has guaranteed nominal coverage of the ITE and provides this coverage with distribution-free and nonasymptotic guarantees. We evaluate the method on synthetic data and illustrate its application in an observational study. Supplementary materials for this article are available online.