  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.
