# Boba: Authoring and Visualizing Multiverse Analyses

arXiv: https://arxiv.org/abs/2007.05551 · HTML: https://mlsanity.com/paper/2007.05551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

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

- [Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships](https://mlsanity.com/paper/2201.02705/) (2022)
- [Sanity Checks for Agentic Data Science](https://mlsanity.com/paper/2604.11003/) (2026)
- [User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics](https://mlsanity.com/paper/2101.02244/) (2021)
- [Intelligent Canvas: Enabling Design-Like Exploratory Visual Data Analysis with Generative AI through Rapid Prototyping, Iteration and Curation](https://mlsanity.com/paper/2402.08812/) (2024)
- [ForkSCOPE: Charting the Agentic Garden of Forking Paths](https://mlsanity.com/paper/2609.12438/) (2026)

## Abstract (arXiv)

Multiverse analysis is an approach to data analysis in which all "reasonable" analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency. However, specifying a multiverse is demanding because analysts must manage myriad variants from a cross-product of analytic decisions, and the results require nuanced interpretation. We contribute Boba: an integrated domain-specific language (DSL) and visual analysis system for authoring and reviewing multiverse analyses. With the Boba DSL, analysts write the shared portion of analysis code only once, alongside local variations defining alternative decisions, from which the compiler generates a multiplex of scripts representing all possible analysis paths. The Boba Visualizer provides linked views of model results and the multiverse decision space to enable rapid, systematic assessment of consequential decisions and robustness, including sampling uncertainty and model fit. We demonstrate Boba's utility through two data analysis case studies, and reflect on challenges and design opportunities for multiverse analysis software.