VISPUR: Visual Aids for Identifying and Interpreting Spurious
  Associations in Data-Driven Decisions
Xian Teng Yongsu Ahn Yu-Ru Lin
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 VISPUR, a
visual analytic system that provides a causal analysis framework and a
human-centric workflow for tackling spurious associations. These include a
CONFOUNDER DASHBOARD, which can automatically identify possible confounding
factors, and a SUBGROUP VIEWER, 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 REASONING
STORYBOARD, which uses a flow-based approach to illustrate paradoxical
phenomena, as well as an interactive DECISION DIAGNOSIS 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.