Smart visualizations of multivariate knowledge

威廉·普莱费尔（William Playfair，1759-1823）被许多人认为是统计图形学之父，他可能会被现代消费者电子表格软件所迷惑。在单个程序中，可以放置数千条数据，只需单击几次鼠标，即可计算汇总值，并以饼图、条形图、折线图和散点图显示数据分布。此外，对单个数据点的更改可以立即通过统计数据传播到图形，让外行人轻松生成图形。但他也可能会惊讶于有多少可视化是他在有生之年开发的，以及目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，在这个点上，有可能在真实的时间里，从观测数据中自动生成关于大量变量之间的概率和因果关系的复杂图表，以直观的方式将这种关系可视化，识别人类生成图表的弱点和优点。（在计算机生成的图表不明确或不完整的情况下），并有效地使用这些图表来理解政策变化的潜在后果。目前的工作借鉴并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成果，以产生算法和技术，为普通用户提供数据。（即，不是受过训练的统计学家的人）直观的视觉界面，用于有效地探索数据集的推测空间，必要时互动。一个概念验证软件的实施已经存在，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.