III: Small: Causal and Statistical Inference in the Presence of Confounding Factors

III：小：存在混杂因素时的因果和统计推断

• 批准号: 1320589
• 负责人: Eleazar Eskin
• 金额: $ 49.99万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320589&HistoricalAwards=false
• 关键词: III; Small; Causal; Statistical; Inference

Technical:The presence of unmeasured confounding factors can result in incorrect statistical and causal inferences if the confounding factors are correlated with the observed data. This phenomenon has been well documented in at least two important applications. One application is identifying genetic variation involved in disease from populations of related individuals. A second application is identifying genes active in a disease when comparing disease and health samples. In this proposal we propose a new approach to correct for unobserved confounders in taking advantage of insights into how confounders affect high dimensional data. These insights motivate a formal definition for a specific type of confounder which we term a 'low-rank confounder.' Formalizing this definition allows us to motivate methods for correcting for the effects of these types confounders even when the confounders are not observed. Our proposal will develop a theory of how confounders affect data and under what conditions unobserved confounders can be corrected. The proposed theory is related to recent developments in understanding sparsity which has been well studied in electrical engineering, computer science and statistics. The result of our proposed methods will lead to improved methods for applications where such confounders are present.Non-technical:Inference of knowledge from high dimensional data is a fundamental problem affecting virtually all areas of science including physics, astronomy, chemistry, computer science, social science and many areas of biology. Many of these problems are driven by recently available large sources of data and advances in measurement or data collection technologies. A major challenge is the presence of unknown (and unmeasured) confounding factors. Confounding factors are variables that are often not observed in the data, but are correlated with various features of the data. Unfortunately, confounding factors can cause incorrect inferences. This phenomenon has been well documented in at least two important applications: one application is identifying genetic variation involved in disease from populations of related individuals, and a second application is identifying genes active in a disease when comparing disease and health samples. There are traditional approaches to perform inference if the confounders are observed in the data. However, dealing with unobserved confounders is more difficult. This project will develop and study a new approach to correct for unobserved confounders, taking advantage of insights into how confounders affect high dimensional data. The project has broad impact due to its utility in a wide range of scientific questions, through the interdisciplinary research opportunities provided to undergraduate and graduate students, and through the distribution of software and data.

技术：如果混杂因素与观察到的数据相关，则存在未衡量的混杂因素可能会导致不正确的统计和因果推断。在至少两个重要的应用中，这种现象已被充分记录。一种应用是确定相关个体人群中疾病中涉及的遗传变异。第二种应用是在比较疾病和健康样本时识别疾病中活跃的基因。在此提案中，我们提出了一种新方法，以纠正未观察到的混杂因素，以利用对混杂因素如何影响高维数据的见解。这些见解激发了对特定类型的混杂因素的形式定义，我们称其为“低级混杂因素”。正式化此定义使我们能够激励方法来纠正这些类型的效果，即使没有观察到混杂因素。我们的建议将发展一个理论，说明混杂因素如何影响数据，并且在什么条件下未观察到的混杂因素可以纠正。拟议的理论与了解稀疏性的最新发展有关，这在电气工程，计算机科学和统计学方面已经很好地研究了。我们提出的方法的结果将导致对存在这种混杂因素的应用的改进方法。Non-technical：从高维数据中推断知识的推断是一个基本问题，影响了几乎所有科学领域，包括物理，化学，化学，计算机科学，社会科学和许多生物学领域。这些问题中有许多是由最近可用的大量数据来源和测量或数据收集技术的进步驱动的。一个主要的挑战是存在未知（未衡量的）混杂因素。混杂因素是数据中通常不会观察到的变量，而是与数据的各种特征相关。不幸的是，混杂因素可能导致不正确的推论。在至少两个重要的应用中，这种现象已得到充分证明：一种应用是识别相关个体种群中疾病中涉及的遗传变异，第二次应用是在比较疾病和健康样本时识别疾病中活跃的基因。如果在数据中观察到混杂因素，则有传统的方法可以执行推断。但是，与未观察到的混杂因素打交道更加困难。该项目将开发和研究一种新的方法来纠正未观察到的混杂因素，利用对混杂因素如何影响高维数据的见解。由于其在各种科学问题上，通过提供给本科生和研究生的跨学科研究机会以及通过软件和数据的分发，该项目在广泛的科学问题上产生了广泛的影响。