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

叙述性简要说明 随着图像、文本和其他复杂格式的出现， 可用于与健康相关的结果。通过在以下背景下考虑这些数据， 观察性因果推理，它们可以用来改善临床决策，帮助评估 通过评估个体化治疗效果，帮助开发智能 可以部署个性化治疗的治疗系统。 在R01EB 025021中，我们重点了解如何在 大量分类协变量的存在。建议的方法（称为火焰- 快速大几乎完全匹配）能够快速了解哪些分类协变量是 重要的是生产高质量的火柴。的 R01EB 025021拟议工作的主要不足之处是，它没有自然地扩展到更多的 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。这个 该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 使得匹配的单元倾向于提供治疗的准确估计 效果MALTS可以使用深度学习来编码单元的潜在表示，或者它可以 学习线性空间中的基变换（拉伸和旋转矩阵）， 数据类型 我们将开发MALTS算法，并将其应用于医学领域。我们的目标是建设高 以下类型的数据的质量匹配：（i）医学图像，例如X射线和CT扫描，（ii） 医疗记录数据，（iii）时间序列数据（连续EEG数据），（iv）第一数据和第二数据中的任何一个的组合， 三种数据。我们的目标是利用新开发的工具，继续评估 隔离流感样疾病的有效性，并将其更广泛地应用于公众可获得的 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.