Abstract
Bayesian nets (BNs) appeared in the 1980s as a solution to computational and representational problems encountered in knowledge representation of uncertain information. Shortly afterwards, BNs became an important part of the AI mainstream. During the 1990s, a lively discussion emerged regarding the causal semantics of Bayesian nets, challenging almost a century of statistical orthodoxy regarding inference of causal relations from observational data, and many refer to BNs now as causal graphs. However, the discussion of causal graphs as a data visualization tool has been limited. We argue here that causal graphs together with their causal semantics for seeing and setting, have the potential to be as powerful and generic a data visualization tool as line graphs or pie charts.
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Neufeld, E., Kristtorn, S. (2005). Picturing Causality – The Serendipitous Semiotics of Causal Graphs. In: Butz, A., Fisher, B., Krüger, A., Olivier, P. (eds) Smart Graphics. SG 2005. Lecture Notes in Computer Science, vol 3638. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536482_24
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DOI: https://doi.org/10.1007/11536482_24
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