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Causal Reasoning with Neuron Diagrams

TitleCausal Reasoning with Neuron Diagrams
Publication TypeConference Paper
Year of Publication2010
AuthorsErwig, M., and E. Walkingshaw
Conference NameIEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
Pagination101 - 108
Date Published09/2010
PublisherIEEE
Conference LocationLeganes, Madrid, Spain
ISBN Number978-1-4244-7621-3
Keywordscausation, neuron diagrams, visual languages
Abstract

The principle of causation is fundamental to science and society and has remained an active topic of discourse in philosophy for over two millennia. Modern philosophers often rely on ``neuron diagrams'', a domain-specific visual language for discussing and reasoning about causal relationships and the concept of causation itself. In this paper we formalize the syntax and semantics of neuron diagrams. We discuss existing algorithms for identifying causes in neuron diagrams, show how these approaches are flawed, and propose solutions to these problems. We separate the standard representation of a dynamic execution of a neuron diagram from its static definition and define two separate, but related semantics, one for the causal effects of neuron diagrams and one for the identification of causes themselves. Most significantly, we propose a simple language extension that supports a clear, consistent, and comprehensive algorithm for automatic causal inference.

DOI10.1109/VLHCC.2010.23