Paper ID: 2203.15274

Finding Structure and Causality in Linear Programs

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

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.

Submitted: Mar 29, 2022