Machine Learning and Deep Learning Solutions Supplement: Matching Methods for Causal Inference with Complex Data

叙事性总结 数据格式的版图正在迅速扩大，包括图像、文本和其他复杂格式 能够获得与健康相关的结果。通过在以下上下文中考虑此类数据 观察性因果推断，它们可以被用来改善临床决策，帮助评估 通过评估个体化治疗效果来评估治疗效果，并帮助开发智能 可部署个性化治疗的治疗系统。 在R01EB025021中，我们专注于了解如何在 存在大量的分类协变量。拟议的方法(称为火焰- 快速、大且几乎完全匹配)能够快速学习哪些范畴协变量 重要的是要生产出高质量的火柴\ciep{wang2017flaming，dieng2018塌陷}。这个 R01EB025021的拟议工作的主要不足是它不能自然地扩展到更多 复杂的数据类型，它只适用于每个特征都有意义的分类数据。{\bf这 提案将开发新的统计和计算工具，用于复杂数据的因果分析 结构。} 我们的新方法被称为学习拉伸后的匹配(MALTS)。对于每个单元(例如 患者)，我们建议学习他们的协变量信息的潜在表示和距离度量 在潜在空间上，匹配的单元往往提供治疗的准确估计 效果。MALTS可以使用深度学习对单元的潜在表示进行编码，或者它可以 学习线性空间中的基变换(拉伸和旋转矩阵)以实现更简单的连续 数据类型。 我们将开发MALTS算法，并将其应用于医学背景。我们的目标是建造高地 以下类型数据的质量匹配：(一)医学图像，如X光和CT扫描，(二) 病历数据，(Iii)时间序列数据(连续脑电数据)，(Iv)第一 三种类型的数据。我们的目标是利用新开发的工具继续评估 隔离治疗流感样疾病的有效性以及将其更广泛地应用于公共可用 现代数据集，如MIMIC III数据库。

NARRATIVE SUMMARY The landscape of data formats is rapidly expanding, with image, text and other complex formats becoming available for health related outcomes. By considering such data within the context of observational causal inference, they can be leveraged to improve clinical decisions, help evaluate treatment efficacy by estimating individualized treatment effects and help develop intelligent therapeutic systems where individualized treatments can be deployed. In R01EB025021, we concentrate on understanding how nearly exact matching can be achieved in the presence of a large number of categorical covariates. The proposed approach (called FLAME - Fast Large Almost Matching Exactly) is able to quickly learn which categorical covariates are important and to produce high quality matches \citep{wang2017flame,dieng2018collapsing}. The main shortfall in the proposed work for R01EB025021 is that it does not naturally extend to more complex data types, it only works for categorical data in which each feature is meaningful. {\bf This proposal will develop new statistical and computational tools for causal analysis of complex data structures.} Our new approach is called {\emph Matching After Learning to Stretch (MALTS)}. For each unit (e.g. patient), we propose learn a latent representation of their covariate information and a distance metric on the latent space such that units that are matched tend to provide accurate estimates of treatment effect. MALTS can use deep learning to encode the latent representations for the units, or it can learn basis transformations in linear space (stretching and rotation matrices) for simpler continuous data types. We will develop the MALTS algorithm, and apply it in a medical context. Our goal is to construct high quality matches for the following types of data: (i) medical images, such as x-rays and CT scans, (ii) medical record data, (iii) time series data (continuous EEG data), (iv) a combination of any of the first three types of data. We aim to leverage the newly developed tools to continue our evaluation of the efficacy of isolation for flu-like ailments as well as to apply them more broadly to publicly available modern datasets such as the MIMIC III database.