Conceptio
›
Archive
›
arXiv (All)
arXiv (All)
open access
CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
Tan, Jiyuan et al.
arXiv (All) · Papers · License: Open Access
Open Source ↗
Direct PDF ↓
econometrics
machine learning, artificial intelligence, econometrics
This document is indexed with metadata only — full text is not available in the archive for this record.
Open the official source ↗
Related documents
Stringological sequence prediction III: layered ziplines and a tradeoff between efficiency and expressivity
#1014985
Intrinsic Sequence-Likelihood Confidence in Retrieval-Dominated Extractive QA: Two Pre-Specified Negatives, and What They Do and Do Not Attribute
#1014987
MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation
#1014989
Not All AI Agents Are Equal: Characterizing Resource and Performance Dynamics
#1014992
Record
· ID 665876
Retrieved via
Conceptio
— every document is proof-bundled with source, license, and retrieval metadata.