V: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions

• Published in: IEEE transactions on visualization and computer graphics Vol. 30; no. 1; pp. 219 - 229
• Main Authors: Teng, Xian, Ahn, Yongsu, Lin, Yu-Ru
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Big Data; Causal Analysis; Cognition; Confounding (Statistics); Decision making; Decisions; Empirical analysis; Heterogeneity; Interviews; Machine learning; Paradoxes; Simpson's Paradox; Spurious Associations; Subgroups; Systematics; Training; Visual aids; Visual analytics; Workflow
• ISSN: 1077-2626; 1941-0506
• DOI: 10.1109/TVCG.2023.3326587

Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's paradox is such a phenomenon where aggregated and subgroup-level associations contradict with each other, causing cognitive confusions and difficulty in making adequate interpretations and decisions. Existing tools provide little insights for humans to locate, reason about, and prevent pitfalls of spurious association in practice. We propose V ispur , a visual analytic system that provides a causal analysis framework and a human-centric workflow for tackling spurious associations. These include a C onfounder D ashboard , which can automatically identify possible confounding factors, and a S ubgroup V iewer , which allows for the visualization and comparison of diverse subgroup patterns that likely or potentially result in a misinterpretation of causality. Additionally, we propose a R easoning S toryboard , which uses a flow-based approach to illustrate paradoxical phenomena, as well as an interactive D ecision D iagnosis panel that helps ensure accountable decision-making. Through an expert interview and a controlled user experiment, our qualitative and quantitative results demonstrate that the proposed "de-paradox" workflow and the designed visual analytic system are effective in helping human users to identify and understand spurious associations, as well as to make accountable causal decisions.