#environmentalists' favorite #statistic... #HazelThayer #yt

https://www.youtube.com/watch?v=1pla_sEjwnk

Title: P4: Causal LLM or splitting LLM [2025-09-11 Thu]

- **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 #causalinference #causality #inference #statistic #observability #llm #reasoning

Title: P3: Causal LLM or splitting LLM [2025-09-11 Thu]

- 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). #causalinference #causality #inference #statistic #observability #llm #reasoning

Title: P2: Causal LLM or splitting LLM [2025-09-11 Thu]

Transparency: Modular, explainable // Opaque, explanation
varies
Scalability: Harder (custom/domain) // Easier
(generalizable)
Data types: Model integration required // Prompting in
one model

**LLM Limitations:** LLMs use pattern matching over
explicit causal modeling.
- No explicit causal graphs/mechanisms—only patterns and
correlations. #causalinference #causality #inference #statistic #observability #llm #reasoning

Title: P1: Causal LLM or splitting LLM [2025-09-11 Thu]

needing rigorous, transparent causality.
- Effective for quick prototyping or low-risk tasks where
simulated causal logic suffices.

Causal Inference Neural Networks vs Prompt-Engineered Multimodal LLM
Causality: Explicit, modeled, testable // Pattern-based,
plausible, implicit
Reliability: High (good data/model) // Medium,
errors/hallucinations #causalinference #causality #inference #statistic #observability #llm #reasoning

Title: P0: Causal LLM or splitting LLM [2025-09-11 Thu]

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 #causalinference #causality #inference #statistic #observability #llm #reasoning