file:https://try-codeberg.github.io/static/causal-inference.gif
Topic: Causal LLM or splitting LLM
Causal Cooperative Networks (CCNets) – Causal Learnign
Framework – Reasoner, Explainer, Producer.
Causal inference finds causes by showing they covary with
effects, occur beforehand, and by ruling out
alternatives.
LLMs use pattern matching, not explicit causal models or
separate reasoning modules.
- Insufficient for regulated or high-stakes domains
needing rigorous, transparent causality. - Effective for quick prototyping or low-risk tasks where
simulated causal logic suffices.
| | **Causal Inference Neural Networks** | **Prompt-Engineered Multimodal LLM** |
|--------------+--------------------------------------+-------------------------------------------|
| Causality | Explicit, modeled, testable | Pattern-based, plausible but implicit |
| Reliability | High (given good data/model) | Medium, can produce errors/hallucinations |
| Transparency | Modular, explainable | Opaque, explanation quality varies |
| Scalability | Harder (custom per domain/signal) | Easier (generalizable across domains) |
| Data types | Requires model integration | Handles via prompting in one model |
LLM Limitations: LLMs use pattern matching over
explicit causal modeling.
- No explicit causal graphs/mechanisms—only patterns and
correlations. - Lack modular separation, functions are entwined.
- Risk of hallucinated causal links, unreliable for
interventions. - Formal counterfactuals need extensive external
scaffolding.
Fields:
-
Healthcare: Predict treatment outcomes (reasoner),
explain intervention effects (explainer), recommend
actions (producer). -
Economics/Policy: Assess impacts, clarify causal
pathways, propose policies. -
Recommendation Systems: Infer preferences, explain
choices, personalize outputs.
Text of original post: https://try-codeberg.github.io/static/causal-inference.org
