Smart visualizations of multivariate knowledge

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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.