Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts

Stokes, Chase; Setlur, Vidya; Cogley, Bridget; Satyanarayan, Arvind; Hearst, Marti

doi:10.48550/arxiv.2208.01780

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…In future work, researchers can further explore how annotations might be incorporated into the various visualization modules to better support communication and collaboration needs during causal inference. Existing studies such as [56] and [7] can also inform the design of these annotation features.…”

Section: Discussionmentioning

confidence: 99%

Causalvis: Visualizations for Causal Inference

Guo

Karavani

Endert

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Causal inference is a statistical paradigm for quantifying causal effects using observational data. It is a complex process, requiring multiple steps, iterations, and collaborations with domain experts. Analysts often rely on visualizations to evaluate the accuracy of each step. However, existing visualization toolkits are not designed to support the entire causal inference process within computational environments familiar to analysts. In this paper, we address this gap with Causalvis, a Python visualization package for causal inference. Working closely with causal inference experts, we adopted an iterative design process to develop four interactive visualization modules to support causal inference analysis tasks. The modules are then presented back to the experts for feedback and evaluation. We found that Causalvis effectively supported the iterative causal inference process. We discuss the implications of our findings for designing visualizations for causal inference, particularly for tasks of communication and collaboration.

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Section: Discussionmentioning

confidence: 99%