# Learning Interpretable Feature Context Effects in Discrete Choice

arXiv: https://arxiv.org/abs/2009.03417 · HTML: https://mlsanity.com/paper/2009.03417/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations](https://mlsanity.com/paper/2101.07730/) (2021)
- [Graph-Based Methods for Discrete Choice](https://mlsanity.com/paper/2205.11365/) (2022)
- [Transformer Choice Net: A Transformer Neural Network for Choice Prediction](https://mlsanity.com/paper/2310.08716/) (2023)
- [Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective](https://mlsanity.com/paper/2501.18282/) (2025)
- [Choice Set Confounding in Discrete Choice](https://mlsanity.com/paper/2105.07959/) (2021)
- [Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling](https://mlsanity.com/paper/2112.15575/) (2021)
- [DeepHalo: A Neural Choice Model with Controllable Context Effects](https://mlsanity.com/paper/2601.04616/) (2026)
- [Pacos: Modeling Users' Interpretable and Context-Dependent Choices in Preference Reversals](https://mlsanity.com/paper/2303.05648/) (2023)
- [Measuring IIA Violations in Similarity Choices with Bayesian Models](https://mlsanity.com/paper/2508.14615/) (2025)
- [Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena](https://mlsanity.com/paper/2610.19095/) (2026)

## Abstract (arXiv)

The outcomes of elections, product sales, and the structure of social connections are all determined by the choices individuals make when presented with a set of options, so understanding the factors that contribute to choice is crucial. Of particular interest are context effects, which occur when the set of available options influences a chooser's relative preferences, as they violate traditional rationality assumptions yet are widespread in practice. However, identifying these effects from observed choices is challenging, often requiring foreknowledge of the effect to be measured. In contrast, we provide a method for the automatic discovery of a broad class of context effects from observed choice data. Our models are easier to train and more flexible than existing models and also yield intuitive, interpretable, and statistically testable context effects. Using our models, we identify new context effects in widely used choice datasets and provide the first analysis of choice set context effects in social network growth.