# Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships

arXiv: https://arxiv.org/abs/2201.02705 · HTML: https://mlsanity.com/paper/2201.02705/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 68 references that are in the map.
- First posted on arXiv 7 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Automatic Construction and Natural-Language Description of Nonparametric Regression Models](https://mlsanity.com/paper/1402.4304/) (2014)
- [Designing and Deploying Online Field Experiments](https://mlsanity.com/paper/1409.3174/) (2014)
- [Causal Relational Learning](https://mlsanity.com/paper/2004.03644/) (2020)
- [Tea: A High-level Language and Runtime System for Automating Statistical Analysis](https://mlsanity.com/paper/1904.05387/) (2019)
- [Paths Explored, Paths Omitted, Paths Obscured: Decision Points & Selective Reporting in End-to-End Data Analysis](https://mlsanity.com/paper/1910.13602/) (2019)
- [Hypothesis Formalization: Empirical Findings, Software Limitations, and Design Implications](https://mlsanity.com/paper/2104.02712/) (2021)
- [Boba: Authoring and Visualizing Multiverse Analyses](https://mlsanity.com/paper/2007.05551/) (2020)

## Built on by (most cited first, 5 of 5)

- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [Can GPT-4 Replicate Empirical Software Engineering Research?](https://mlsanity.com/paper/2310.01727/) (2023)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks](https://mlsanity.com/paper/2507.02819/) (2025)
- [How to Run Statistics over LLM Judges and Trust the Results: Calibrated Inference for Small-Sample AI Evaluation with evalstats](https://mlsanity.com/paper/2609.35815/) (2026)

## Abstract (arXiv)

Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured. However, data analysts currently lack tool support for recording and reasoning about domain assumptions, data collection, and modeling choices in an integrated manner, leading to mistakes that can compromise scientific validity. For instance, generalized linear mixed-effects models (GLMMs) help answer complex research questions, but omitting random effects impairs the generalizability of results. To address this need, we present Tisane, a mixed-initiative system for authoring generalized linear models with and without mixed-effects. Tisane introduces a study design specification language for expressing and asking questions about relationships between variables. Tisane contributes an interactive compilation process that represents relationships in a graph, infers candidate statistical models, and asks follow-up questions to disambiguate user queries to construct a valid model. In case studies with three researchers, we find that Tisane helps them focus on their goals and assumptions while avoiding past mistakes.