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

arXiv: https://arxiv.org/abs/2307.14448 · HTML: https://mlsanity.com/paper/2307.14448/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 86 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 27 of 27)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [The What-If Tool: Interactive Probing of Machine Learning Models](https://mlsanity.com/paper/1907.04135/) (2019)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning](https://mlsanity.com/paper/1904.05419/) (2019)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [FairSight: Visual Analytics for Fairness in Decision Making](https://mlsanity.com/paper/1908.00176/) (2019)
- [Orthogonal Random Forest for Causal Inference](https://mlsanity.com/paper/1806.03467/) (2018)
- [DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models](https://mlsanity.com/paper/2008.08353/) (2020)
- [Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments](https://mlsanity.com/paper/1905.10176/) (2019)
- [Boxer: Interactive Comparison of Classifier Results](https://mlsanity.com/paper/2004.07964/) (2020)
- [Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens](https://mlsanity.com/paper/2210.14011/) (2022)
- [An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference](https://mlsanity.com/paper/1906.00442/) (2019)
- [Visual Causality Analysis of Event Sequence Data](https://mlsanity.com/paper/2009.00219/) (2020)
- [A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications](https://mlsanity.com/paper/2009.02458/) (2020)
- [Causalvis: Visualizations for Causal Inference](https://mlsanity.com/paper/2303.00617/) (2023)
- [VAINE: Visualization and AI for Natural Experiments](https://mlsanity.com/paper/2109.04348/) (2021)
- [RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups](https://mlsanity.com/paper/2209.06378/) (2022)
- [Elliptical Insights: Understanding Statistical Methods through Elliptical Geometry](https://mlsanity.com/paper/1302.4881/) (2013)
- [Causal Support: Modeling Causal Inferences with Visualizations](https://mlsanity.com/paper/2107.13485/) (2021)
- [NOVA: A Practical Method for Creating Notebook-Ready Visual Analytics](https://mlsanity.com/paper/2205.03963/) (2022)
- [Computational Social Scientist Beware: Simpson's Paradox in Behavioral Data](https://mlsanity.com/paper/1710.08615/) (2017)
- [DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways](https://mlsanity.com/paper/1904.11652/) (2019)
- [CausalFlow: Visual Analytics of Causality in Event Sequences](https://mlsanity.com/paper/2008.11899/) (2020)
- [Taggle: Combining Overview and Details in Tabular Data Visualizations](https://mlsanity.com/paper/1712.05944/) (2017)

## Built on by (most cited first, 1 of 1)

- [Human-Guided Causal Knowledge Injection for Virtual Cells](https://mlsanity.com/paper/2608.08430/) (2026)

## Abstract (arXiv)

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.