A review of UK MHRA protocols (k=122) of the use of #Estimand -s offers insights for improvement of protocols due to incomplete or incorrect specifications:
https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-025-08991-8

#RCT #StudyDesign #Registration #Causality

Further resources:

How estimands support stating the exact research question of a study and the interpretation of results:
https://www.bmj.com/content/384/bmj-2023-076316

Application of the estimand framework to for studies with Patient-Reported Outcomes:
https://jpro.springeropen.com/articles/10.1186/s41687-020-00218-5
#HRQOL

Baoyu Sun et al. explored whether #ClimateWarming will reshape the cause-and-effect interactions between coastal plant productivity and #SoilFactors.

#Causality | #GrossPrimaryProductivity | #ConvergentCrossMapping | #CoastalWetland

https://doi.org/10.1093/jpe/rtae107

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

🧠 “Correlation ≠ causation”? This new study put it to the test: activity in posterior parietal #cortex not only correlates with navigation choices, it is causal. Using a #BrainMachineInterface, mice could steer solely from these signals.

🌍 https://www.cell.com/cell-reports/fulltext/S2211-1247(25)00633-3

#Neuroscience #SystemsNeuro #Causality #DecisionMaking #BMI #correlation

Recent @DSLC club meetings:

  Devops for Data Science: Data Project Architecture https://youtu.be/Zpv4TkwHgAc #RStats #PyData #DevOps

From the @DSLC ​chives:

 The Effect: An Introduction to Research Design and Causality: Matching https://youtu.be/bD20P1XPgNk #RStats #causal #causality

 RWTF: API for an analysis https://youtu.be/o0-LfV6Tcjg #RStats

Visit https://dslc.video for hours of new #DataScience videos every week!