Conference: Drawing Causal Inference from Big Data

2015年3月26日和27日，一场名为“从大数据中得出因果关系”的会议将在华盛顿的国家科学院礼堂举行。本次会议的目的是提出最先进的方法来解决这个问题，并汇集领先的专家，无论是专题发言人和其他专家，谁将通过他们的互动产生的进展。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，因为大数据通常是以不受控制的方式在现场收集的，并且因为数据的庞大规模，与流行的看法相反，使识别因果效应变得更加困难。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，计算技术很可能最好被视为增强而不是取代人类的洞察力：目前的算法可以找到复杂的模式和关联，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收集的大量数据中定义因果关系的适当方法，以及找到测量变量之外的原因的方法。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未测量的环境因素。会议的主题是至关重要的和当前的利益，研究，商业和政府机构的每一个领域。我们的社会已经开发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实验，但大多数情况下来自相对不受控制的实地观察，例如来自社交网络、人类医学和遗传测量以及购买模式的观察。数据的数量已经远远超出了我们辨别数据中哪些是重要模式的能力，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，有大量的变量可以被测量，这些变量的不同子组之间的相关性几乎不可计数。如果大数据中的关键模式不仅可以确定，而且可以解释，那么科学，商业，政府和社会都将获得巨大的潜在利益。解释是本次会议的目标，以“因果推理”为代表。“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率的下降有关，但需要知道这种治疗是否是导致癌症死亡率下降的原因。在教育方面，我们可能会观察到，在低年级被留级的学生往往会从高中辍学，但我们需要知道这种治疗是否会导致这种结果。

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.