Causal LLM or splitting LLM

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