Research Paper#Causal Inference, Probabilistic Modeling, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 18:53
Probabilistic Modeling for Causal Inference
Published:Dec 29, 2025 12:07
•1 min read
•ArXiv
Analysis
This paper challenges the notion that specialized causal frameworks are necessary for causal inference. It argues that probabilistic modeling and inference alone are sufficient, simplifying the approach to causal questions. This could significantly impact how researchers approach causal problems, potentially making the field more accessible and unifying different methodologies under a single framework.
Key Takeaways
- •Causal inference can be performed using only probabilistic modeling and inference.
- •No need for specialized causal frameworks or notation.
- •Causal tools can be reinterpreted as emerging from standard probabilistic methods.
Reference
“Causal questions can be tackled by writing down the probability of everything.”