Principled Methods for Very Large-Scale Causal Discovery

描述（由申请人提供）： 本文提出的研究的长期目标是开发、验证和应用用于非常大规模的原则性因果发现的方法，这些方法可以扩展到大规模的数据集，例如生物信息学、电子病历和书目系统中的数据集。这些数据集的爆炸性增殖和增长（在样本，变量和质量方面）为生物医学发现创造了巨大的机会，因此用于因果发现的强大方法有可能彻底改变生物医学。 为了解决这个规模问题，co-PI开发了几种新颖的因果发现算法，这些算法具有明确定义的属性和保证，采用了原则性的局部方法：这些算法只关注局部因果邻域因果关系是指单个或多个“目标”变量的因果关系（例如，直接原因和影响，或者可替代地，马尔可夫毯），并且它们建立在用于表示和学习因果关系的正式框架上。大量的模拟和真实的数据的初步实验表明，该算法是健全的和高度可扩展的。 本地算法，通过他们的假设，预计具有广泛的应用背景，包括生物信息学，流行病学，文本分析和临床医学的适用性。拟议的研究打算在这个广阔的应用空间中采取两个重点步骤。局部算法将应用于（a）肺癌患者的基因表达数据和（B）非侵袭性乳腺疾病患者中影响乳腺癌发展因素的大型流行病学分析数据。假设将发现新的和潜在重要的新因果关系。这一假说具有重要的生物医学和方法学意义。具体目标是（i）验证新的因果算法;（ii）诱导新的假设的直接原因和影响的一组选定的基因涉及肺癌;（iii）诱导新的因果假设的原因乳腺癌;（iv）比较性能的新的本地算法，以国家的最先进的替代品;（v）传播新的和强大的因果发现工具。评估新因果算法及其产生的假设的方法是：（a）使用领域专家的结构化、循证、盲法文献综述对现有知识进行验证;（B）在细胞系（肺癌领域）中进行选择性实验;（c）统计性能指标。

DESCRIPTION (provided by applicant): The long-term goal of the research proposed here is to develop, validate and apply methods for very large-scale principled causal discovery that scale up to massive datasets such as the ones found in bioinformatics, electronic patient records, and bibliographic systems. The explosive proliferation and growth (in sample, variables, and quality) of such datasets creates tremendous opportunities for biomedical discoveries, hence powerful methods for causal discovery have the potential to revolutionize biomedicine. To address this problem of scale, the co-PIs have developed several novel causal discovery algorithms with well-defined properties and guarantees that employ a principled local approach: these algorithms focus only on the local causal neighborhood (e.g. direct causes and effects or, alternatively, Markov Blanket) of a single or several "target" variable(s), and they are built on a formal framework for representing and learning causality. A plethora of preliminary experiments with simulated and real data suggest that the algorithms are sound and highly scalable. The local algorithms, by their assumptions, are expected to have applicability to a broad application context that includes bioinformatics, epidemiology, text analysis, and clinical medicine. The proposed research intends to take two focused steps in this broad application space. The local algorithms will be applied to (a) gene expression data from patients with lung cancer and (b) data from a large epidemiologic analysis of factors that influence development of breast cancer in patients with non-invasive breast disease. It is hypothesized that novel and potentially significant new causal relationships will be discovered. This hypothesis bears great biomedical and methodological significance. The specific aims are to (i) validate the novel causal algorithms; (ii) induce novel hypotheses about the immediate causes and effects of a selected group of genes implicated in lung cancer; (iii) induce novel causal hypotheses about the causes of breast cancer; (iv) compare the performance of the novel local algorithms to state-of-the-art alternatives; (v) disseminate new and powerful causal discovery tools. The methods to evaluate the novel causal algorithms and the hypotheses generated by them are: (a) validation against existing knowledge using structured, evidence-based, blinded literature review by domain experts; (b) selective experimentation in cell lines (lung cancer domain), and (c) statistical performance metrics.