Finding Structure and Causality in Linear Programs

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

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arXiv · 2203.15274

Finding Structure and Causality in Linear Programs

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

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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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Matej Zečević, Florian Peter Busch, Devendra Singh Dhami, Kristian Kersting. 2022-03-29. Finding Structure and Causality in Linear Programs. https://arxiv.org/abs/2203.15274

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