# Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design

arXiv: https://arxiv.org/abs/2310.12026 · HTML: https://mlsanity.com/paper/2310.12026/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models](https://mlsanity.com/paper/1703.07370/) (2017)
- [Off-policy Bandits with Deficient Support](https://mlsanity.com/paper/2006.09438/) (2020)
- [MuProp: Unbiased Backpropagation for Stochastic Neural Networks](https://mlsanity.com/paper/1511.05176/) (2015)
- [DisARM: An Antithetic Gradient Estimator for Binary Latent Variables](https://mlsanity.com/paper/2006.10680/) (2020)
- [Probabilistic Best Subset Selection via Gradient-Based Optimization](https://mlsanity.com/paper/2006.06448/) (2020)
- [Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis](https://mlsanity.com/paper/2201.01357/) (2022)
- [Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping](https://mlsanity.com/paper/2208.06124/) (2022)
- [Product Aesthetic Design: A Machine Learning Augmentation](https://mlsanity.com/paper/1907.07786/) (2019)
- [Active Learning for Non-Parametric Choice Models](https://mlsanity.com/paper/2208.03346/) (2022)

## Abstract (arXiv)

Designing products to meet consumers' preferences is essential for a business's success. We propose the Gradient-based Survey (GBS), a discrete choice experiment for multiattribute product design. The experiment elicits consumer preferences through a sequence of paired comparisons for partial profiles. GBS adaptively constructs paired comparison questions based on the respondents' previous choices. Unlike the traditional random utility maximization paradigm, GBS is robust to model misspecification by not requiring a parametric utility model. Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers. We demonstrate the advantage of GBS in accuracy and sample efficiency compared to the existing parametric and nonparametric methods in simulations.