When Statisticians Can Also Be AI Engineers, What Could We Build?
Abstract
Statistical software makes methods widely available. Good statistical work, however, often requires conversations that clarify the scientific question, how the data were collected, and what conclusions the evidence can support. Coding agents and reusable AI infrastructure create opportunities for statisticians to incorporate these processes into agentic tools for research, practice, and education. A review of agentic AI applications for data analysis and study design distinguishes general human participation from human interaction guided by statistical principles. We examine how statistical expertise can guide the design and evaluation of domain-specific interactive harnesses. Two practical entry points illustrate how statisticians can begin building: an implemented causal-consulting skill within an existing agent environment and Model Forge, a custom harness for interactive model and algorithm development. These design examples connect statistical principles to choices about context, memory, tools, and interfaces, and motivate evaluation of both computational correctness and the scientific consequences of interaction. We conclude with a broader interdisciplinary research agenda on adaptive human-agent interaction, including selective information acquisition, evolving user preferences, and evaluation under uncertainty.
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BibTeX
@misc{zhu2026when,
title = {{When Statisticians Can Also Be AI Engineers, What Could We Build?}},
author = {Ruoqing Zhu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.32105},
eprint = {2610.32105},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.32105},
}
RIS
TY - UNPB
TI - When Statisticians Can Also Be AI Engineers, What Could We Build?
AU - Ruoqing Zhu
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2610.32105
UR - https://arxiv.org/abs/2610.32105
AB - Statistical software makes methods widely available. Good statistical work, however, often requires conversations that clarify the scientific question, how the data were collected, and what conclusions the evidence can support. Coding agents and reusable AI infrastructure create opportunities for statisticians to incorporate these processes into agentic tools for research, practice, and education. A review of agentic AI applications for data analysis and study design distinguishes general human participation from human interaction guided by statistical principles. We examine how statistical expertise can guide the design and evaluation of domain-specific interactive harnesses. Two practical entry points illustrate how statisticians can begin building: an implemented causal-consulting skill within an existing agent environment and Model Forge, a custom harness for interactive model and algorithm development. These design examples connect statistical principles to choices about context, memory, tools, and interfaces, and motivate evaluation of both computational correctness and the scientific consequences of interaction. We conclude with a broader interdisciplinary research agenda on adaptive human-agent interaction, including selective information acquisition, evolving user preferences, and evaluation under uncertainty.
ER -