Conference: Drawing Causal Inference from Big Data

会议：从大数据中得出因果推论

• 批准号: 1430441
• 负责人: Richard Shiffrin
• 金额: $ 10万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1430441&HistoricalAwards=false
• 关键词: Conference; Drawing; Causal; Inference; Big

A conference titled "Drawing Causal Inference from Big Data" will be held March 26 and 27, 2015, in the National Academy of Sciences auditorium in Washington DC. The purpose of this conference is to present state-of-the-art approaches to the problem, and to bring together leading experts, both the featured speakers and other experts, who will generate progress through their interactions. In many respects the subject of this conference is in its infancy because the many methods that have been developed and used for causal inference in small data do not scale up, because Big Data is often collected in the field in uncontrolled fashion, and because of the sheer size of the data that, contrary to popular belief, make it more rather than less difficult to identify causal effects. The problems in dealing with Big Data are in good part rooted in the limitations of human cognition, so ongoing efforts are aimed at the development of computational algorithms. However it is likely that computational techniques are best viewed as augmenting rather than replacing human insight: Current algorithms can find complex patterns and associations but most are not aimed to discover causal explanations. The conference also addresses the appropriate way to define causality in large data collected from chaotic and noisy systems, and the way to find causes that lie outside the measured variables. For example a correlation observed in a health survey based on genetic mapping might be due to an unmeasured environmental factor such as poverty. The subject of the conference is of vital and current interest to every field of study, business, and government agencies. Our society has developed methods of collecting and storing enormous amounts of data, and is increasingly doing so. The data can arrive from controlled experiments, but most often comes from relatively uncontrolled field observations, such as those from social networks, human medical and genetic measurements, and patterns of purchases. The amount of data has far outstripped our ability to discern what important patterns are in the data, and most important, what explains those patterns. In a typical large database there are huge number of variables that can be measured, and virtually uncountable numbers of correlations between different subgroups of those variables. There are enormous potential benefits to science, business, government, and society if the critical patterns in Big Data can not only be ascertained but explained. Explanation is the goal of this conference, represented by the phrase, "drawing causal inference." The most pressing questions we face are causal in nature. In health we might observe that a particular treatment is associated with a decrease of cancer deaths, but need to know if the treatment is the cause of the decrease. In education we might observe that students held back in early grades tend to drop out of high school, but need to know if the treatment causes that result.

2015年3月26日至27日，将在华盛顿特区的美国国家科学院礼堂举办一场名为“从大数据中得出因果关系”的会议。这次会议的目的是介绍解决这一问题的最先进的方法，并将主要专家聚集在一起，包括专题演讲者和其他专家，他们将通过互动取得进展。在许多方面，这次会议的主题还处于初级阶段，因为已经开发并用于在小数据中进行因果推断的许多方法没有扩大规模，因为大数据通常是以不受控制的方式在现场收集的，而且由于数据的巨大规模，与普遍认为的相反，这使得识别因果关系变得更加困难，而不是更容易。处理大数据的问题在很大程度上源于人类认知的局限性，因此正在进行的努力旨在开发计算算法。然而，很可能计算技术最好被视为增强而不是取代人类洞察力：目前的算法可以发现复杂的模式和关联，但大多数都不是为了发现因果解释。会议还讨论了如何在从混沌和噪声系统收集的大量数据中定义因果关系的适当方法，以及如何找到存在于测量变量之外的原因。例如，在基于基因图谱的健康调查中观察到的相关性可能是由于贫困等不可测量的环境因素造成的。这次会议的主题对各个研究领域、企业和政府机构都具有重要的现实意义。我们的社会已经开发出收集和存储海量数据的方法，并且正在越来越多地这样做。数据可以来自受控实验，但最常见的是来自相对不受控制的现场观察，例如来自社交网络、人类医学和基因测量以及购买模式的数据。数据量已经远远超过了我们辨别数据中哪些重要模式，以及最重要的是，如何解释这些模式的能力。在一个典型的大型数据库中，有大量可以测量的变量，这些变量的不同子组之间的相关性几乎是不可计数的。如果大数据中的关键模式不仅可以被确定，而且可以被解释，那么它将给科学、商业、政府和社会带来巨大的潜在利益。解释是这次会议的目标，用短语“引出因果推理”来代表。我们面临的最紧迫的问题本质上是因果关系。在健康方面，我们可能会观察到某种特定的治疗与癌症死亡人数的减少有关，但需要知道该治疗是否是癌症死亡人数下降的原因。在教育中，我们可能会观察到，早期成绩落后的学生往往会从高中辍学，但需要知道治疗是否导致了这种结果。