Today I came across the central limit theorem once again. And the name already says it: central 😉

 Take many independent random variables & average them.
→ You’ll get something close to a normal distribution.
→ Whatever the originals looked like.

 To try it out in R:
m <- replicate(10000, mean(runif(100, 0, 100)))
hist(m, main="Distribution of means (CLT)")

 Essential for anyone working with data: In statistics, machine learning or science. And it turns out to be easier to understand than you might think.

#data #datascience #datascientist #math #statistic #machinelearning #ai #ki #artificialintelligence #science #students

P2: #causalinference #causality #inference #statistic #observability #llm #reasoning
| 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

P2: P1: #causalinference #causality #inference #statistic #observability #llm #reasoning
needing rigorous, transparent causality.
- Effective for quick prototyping or low-risk tasks where
simulated causal logic suffices.

```text
| | **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) |

P1: P1: #causalinference #causality #inference #statistic #observability #llm #reasoning
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