Principled Methods for Very Large-Scale Causal Discovery

描述(由申请人提供)： 这里提出的研究的长期目标是开发、验证和应用超大规模原则性因果发现的方法，这些方法可以扩展到大规模数据集，例如在生物信息学、电子患者记录和书目系统中发现的数据集。这些数据集的爆炸性增长(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，因此强大的因果发现方法有可能给生物医学带来革命性的变化。 为了解决这个规模问题，联合PI开发了几个新的因果发现算法，这些算法具有定义良好的属性和保证，采用了原则性的局部方法：这些算法只关注单个或多个“目标”变量(S)的局部因果邻域(例如，直接因果或马尔可夫毯子)，并且它们构建在表示和学习因果关系的正式框架上。大量的模拟和真实数据的初步实验表明，这些算法是可靠的，并且具有很高的可扩展性。 根据他们的假设，当地的算法预计将适用于广泛的应用背景，包括生物信息学、流行病学、文本分析和临床医学。拟议的研究打算在这一广阔的应用领域采取两个有重点的步骤。本地算法将应用于(A)肺癌患者的基因表达数据和(B)对影响非侵袭性乳腺疾病患者乳腺癌发展的因素进行大规模流行病学分析的数据。假设会发现新的、可能具有重大意义的新因果关系。这一假说具有重要的生物医学和方法论意义。其具体目标是：(I)验证新的因果算法；(Ii)关于与肺癌有关的一组选定基因的直接原因和影响的新的假设；(Iii)关于乳腺癌原因的新的因果假设；(Iv)将新的本地算法的性能与最先进的替代方案进行比较；(V)传播新的和强大的因果发现工具。评估新的因果算法及其产生的假设的方法是：(A)使用结构化的、基于证据的、由领域专家进行的盲目文献审查来对照现有知识进行验证；(B)在细胞系(肺癌领域)中进行选择性实验；以及(C)统计性能度量。

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.