# Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data

arXiv: https://arxiv.org/abs/1805.03094 · HTML: https://mlsanity.com/paper/1805.03094/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 7 of 7)

- [Causal Collaborative Filtering](https://mlsanity.com/paper/2102.01868/) (2021)
- [Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality](https://mlsanity.com/paper/2412.07200/) (2024)
- [DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data](https://mlsanity.com/paper/2108.13581/) (2021)
- [Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community](https://mlsanity.com/paper/2110.07938/) (2021)
- [De-paradox Tree: Breaking Down Simpson's Paradox via A Kernel-Based Partition Algorithm](https://mlsanity.com/paper/2603.02174/) (2026)
- [FAIRLABEL: Correcting Bias in Labels](https://mlsanity.com/paper/2311.00638/) (2023)
- [Simpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics](https://mlsanity.com/paper/2605.11017/) (2026)

## Abstract (arXiv)

We describe a data-driven discovery method that leverages Simpson's paradox to uncover interesting patterns in behavioral data. Our method systematically disaggregates data to identify subgroups within a population whose behavior deviates significantly from the rest of the population. Given an outcome of interest and a set of covariates, the method follows three steps. First, it disaggregates data into subgroups, by conditioning on a particular covariate, so as minimize the variation of the outcome within the subgroups. Next, it models the outcome as a linear function of another covariate, both in the subgroups and in the aggregate data. Finally, it compares trends to identify disaggregations that produce subgroups with different behaviors from the aggregate. We illustrate the method by applying it to three real-world behavioral datasets, including Q&A site Stack Exchange and online learning platforms Khan Academy and Duolingo.