Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
复制标题
连续值干预的因果效应估计的可扩展敏感性和不确定性分析
DOI：
10.48550/arxiv.2204.10022
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Uri Shalit
中科院分区：
文献类型：
--
作者：
A. Jesson;A. Douglas;P. Manshausen;N. Meinshausen;P. Stier;Y. Gal;Uri Shalit
对于气候科学、医疗保健和经济学来说，根据观测数据估计连续价值干预措施的效果是一项至关重要的任务。最近的工作重点是设计神经网络架构和正则化函数，以便根据高维、大样本数据对平均和个体水平的剂量反应曲线进行可扩展的估计。这种方法假设可忽略性（观察所有混杂变量）和积极性（观察描述一组单位的每个协变量值的所有治疗水平），这些假设在连续治疗方案中存在问题。用于理解放松这些假设时因果估计中引起的无知的可扩展敏感性和不确定性分析的研究较少。在这里，我们开发了一个连续治疗效果边际敏感性模型（CMSM），并得出与观察到的数据一致的界限以及研究人员定义的隐藏混杂水平。我们引入了可扩展的算法和不确定性感知深度模型来推导和估计高维、大样本观测数据的这些界限。我们与气候科学家合作，利用过去 15 年的卫星观测结果，研究人类排放对云特性的气候影响。众所周知，这个问题因许多未被观察到的混杂因素而变得复杂。
Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses on designing neural network architectures and regularization functions to allow for scalable estimation of average and individual-level dose-response curves from high-dimensional, large-sample data. Such methodologies assume ignorability (observation of all confounding variables) and positivity (observation of all treatment levels for every covariate value describing a set of units), assumptions problematic in the continuous treatment regime. Scalable sensitivity and uncertainty analyses to understand the ignorance induced in causal estimates when these assumptions are relaxed are less studied. Here, we develop a continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds that agree with the observed data and a researcher-defined level of hidden confounding. We introduce a scalable algorithm and uncertainty-aware deep models to derive and estimate these bounds for high-dimensional, large-sample observational data. We work in concert with climate scientists interested in the climatological impacts of human emissions on cloud properties using satellite observations from the past 15 years. This problem is known to be complicated by many unobserved confounders.
DOI：
--
发表时间：
2018-10
期刊：
ArXiv
影响因子：
--
作者：
Nathan Kallus;Xiaojie Mao;Angela Zhou
通讯作者：
Nathan Kallus;Xiaojie Mao;Angela Zhou
用于限制治疗效果的随机因果规划
DOI：
--
发表时间：
2023
期刊：
--
影响因子：
--
作者：
K. Padh
通讯作者：
K. Padh