Smart visualizations of multivariate knowledge

被许多人认为是统计图形之父的威廉·普莱费尔（William Playfair, 1759-1823）可能会被现代消费者电子表格软件弄得眼花缭乱。在单个程序中，可以放置数千条数据，只需单击几下鼠标，就可以计算汇总值，并以饼状图、条形图、线形图和散点图的形式显示数据分布。此外，对单个数据点的更改可以立即通过统计和图形传播，让外行人员轻松制作图形。但他也可能会惊讶于他一生中开发了多少可视化，以及当前的软件通常一次只能处理几个变量之间的关系。我相信我们正处于研究前沿的一个点上，在这个点上，我们可以实时地自动生成关于观测数据中大量变量之间的概率和因果关系的复杂猜测，以直观的方式可视化这种关系，识别人类产生的猜测的弱点和优势（计算机产生的猜测是模糊的或不完整的），并有效地使用这些图表来理解政策变化的潜在后果。目前的工作借鉴和扩展了我以前的工作成果，以及其他人在人工智能、图形模型、数据可视化、信息可视化和计算机图形学领域的工作，以产生算法和技术，这些算法和技术将为数据的普通用户（即未受过训练的统计学家）提供直观的视觉界面，用于有效地探索数据集的猜想空间，必要时进行交互。一个概念验证软件实现已经存在，初步结果已经公布。

William Playfair (1759-1823), considered by many to be the parent of statistical graphics, would likely have been dazzled by modern consumer spreadsheet software. In a single program, one can place thousands of pieces of data, and with a few mouse clicks, compute summary values, and display distributions of data in pie, bar, line, and scatter graphs. Furthermore, a change to a single data point can be propagated immediately through the statistics and to the graphics, letting lay persons easily produce the graphs. But he also might have been surprised at how many of the visualizations were ones he developed in his lifetime, and at the fact that current software generally manipulates relationships between only a few variables at a time. I believe we are at a point in the research frontier where it is possible, in real time, to automatically generate complex conjectures about probabilistic and causal relationships among large numbers of variables from observational data, to visualize such relationships in an intuitive way, to identify weaknesses and strengths in human generated conjectures (where computer-generated conjectures are ambiguous or incomplete), and to use these diagrams effectively to understand the potential consequences of policy changes. The present work draws on and extend results from my previous work, and the work of others, in the areas of artificial intelligence, graphical models, data visualization, information visualization, and computer graphics to produce algorithms and techniques that will give ordinary users of data (i.e, persons who are not trained statisticians) an intuitive visual interface for productively exploring the conjecture space of datasets, interacting as necessary. A proof of concept software implmentation already exists, and preliminary results have already been published.