# A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling

arXiv: https://arxiv.org/abs/2109.03669 · HTML: https://mlsanity.com/paper/2109.03669/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2021.

## Built on by (most cited first, 2 of 2)

- [Towards an Understanding and Explanation for Mixed-Initiative Artificial Scientific Text Detection](https://mlsanity.com/paper/2304.05011/) (2023)
- [Designing Computational Tools for Exploring Causal Relationships in Qualitative Data](https://mlsanity.com/paper/2602.06506/) (2026)

## Abstract (arXiv)

Modeling complex systems is a time-consuming, difficult and fragmented task, often requiring the analyst to work with disparate data, a variety of models, and expert knowledge across a diverse set of domains. Applying a user-centered design process, we developed a mixed-initiative visual analytics approach, a subset of the Causemos platform, that allows analysts to rapidly assemble qualitative causal models of complex socio-natural systems. Our approach facilitates the construction, exploration, and curation of qualitative models bringing together data across disparate domains. Referencing a recent user evaluation, we demonstrate our approach's ability to interactively enrich user mental models and accelerate qualitative model building.