# Discovering Context Effects from Raw Choice Data

arXiv: https://arxiv.org/abs/1902.03266 · HTML: https://mlsanity.com/paper/1902.03266/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2019.

## Built on by (most cited first, 14 of 14)

- [Learning Rich Rankings](https://mlsanity.com/paper/2312.15081/) (2023)
- [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)
- [A Neural Network Based Choice Model for Assortment Optimization](https://mlsanity.com/paper/2308.05617/) (2023)
- [Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective](https://mlsanity.com/paper/2501.18282/) (2025)
- [On the estimation of discrete choice models to capture irrational customer behaviors](https://mlsanity.com/paper/2109.03882/) (2021)
- [Choice Set Confounding in Discrete Choice](https://mlsanity.com/paper/2105.07959/) (2021)
- [Approximating a RUM from Distributions on k-Slates](https://mlsanity.com/paper/2305.13283/) (2023)
- [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)
- [OTSS: Output-Targeted Soft Segmentation for Contextual Decision-Weight Learning](https://mlsanity.com/paper/2605.00193/) (2026)
- [Consideration Circuits: Depth Separation and Universality Beyond a Single Softmax](https://mlsanity.com/paper/2610.04143/) (2026)
- [Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena](https://mlsanity.com/paper/2610.19095/) (2026)

## Abstract (arXiv)

Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgements can be affected by "irrelevant" aspects of the context in which they are made. An important class of such contexts is the composition of the choice set. In this work, our goal is to discover such choice set effects from raw choice data. We introduce an extension of the Multinomial Logit (MNL) model, called the context dependent random utility model (CDM), which allows for a particular class of choice set effects. We show that the CDM can be thought of as a second-order approximation to a general choice system, can be inferred optimally using maximum likelihood and, importantly, is easily interpretable. We apply the CDM to both real and simulated choice data to perform principled exploratory analyses for the presence of choice set effects.