Conference: Drawing Causal Inference from Big Data

2015年3月26日至27日，一场名为“从大数据中得出因果推论”的会议将在华盛顿特区的国家科学院礼堂举行。本次会议的目的是介绍解决这一问题的最新方法，并汇集主要专家，包括特邀演讲者和其他专家，他们将通过相互作用取得进展。在许多方面，本次会议的主题还处于起步阶段，因为已经开发和用于小数据因果推理的许多方法并没有扩大规模，因为大数据通常是在该领域以不受控制的方式收集的，而且因为数据的绝对规模，与流行的看法相反，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，最好将计算技术视为增强而不是取代人类的洞察力：目前的算法可以发现复杂的模式和关联，但大多数算法的目的不是发现因果解释。会议还讨论了在从混沌和噪声系统中收集的大量数据中定义因果关系的适当方法，以及找到存在于测量变量之外的原因的方法。例如，在基于遗传图谱的健康调查中观察到的相关性可能是由于贫困等未测量的环境因素。会议的主题是至关重要的和当前的兴趣，每个领域的研究，商业和政府机构。我们的社会已经发展了收集和存储大量数据的方法，并且越来越多地这样做。这些数据可以来自受控制的实验，但最常见的是来自相对不受控制的实地观察，比如来自社交网络、人类医学和基因测量以及购买模式的数据。数据的数量远远超过了我们辨别数据中重要模式的能力，最重要的是，如何解释这些模式。在一个典型的大型数据库中，有大量的变量可以测量，而这些变量的不同子组之间的相关性实际上是数不清的。如果大数据中的关键模式不仅能被确定，而且能被解释清楚，那么对科学、商业、政府和社会都有巨大的潜在好处。解释是这次会议的目标，用短语“得出因果推论”来表示。我们面临的最紧迫的问题本质上是因果关系。在健康方面，我们可能会观察到一种特定的治疗方法与癌症死亡率的下降有关，但需要知道这种治疗方法是否是导致死亡率下降的原因。在教育方面，我们可能会观察到，在早期学习中受到阻碍的学生往往会在高中辍学，但我们需要知道这种治疗是否导致了这种结果。

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.