Finding Structure and Causality in Linear Programs

Matej Zečević, Florian Peter Busch, Devendra Singh Dhami, Kristian Kersting

Keywords: linear programs, structure, causality, graph learning

Abstract: Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure on end-to-end learning systems. Their potential might seem depleted but we propose a foundational, causal perspective that reveals intriguing intra- and inter-structure relations for LP components. We conduct a systematic, empirical investigation on general-, shortest path- and energy system LPs.

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