Causality, But Make It Massive: How DEMOCRITUS Turns LLM Chaos into Coherent Causal Maps
Maps are useful because they are not the territory. Nobody opens Google Maps and assumes the blue line has physically repaired the road. Sensible people use it to orient themselves, notice routes, avoid obvious mistakes, and decide where to inspect more carefully. That is the cleanest way to read DEMOCRITUS, the system described in Large Causal Models from Large Language Models.1 It does not make LLMs magically perform causal inference. It does not estimate treatment effects. It does not solve confounding. It does not turn a pile of text into scientific truth by sprinkling geometry on top, though that would be a very efficient way to sell consulting decks to executives with poor impulse control. ...