# Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis

arXiv: https://arxiv.org/abs/2201.01357 · HTML: https://mlsanity.com/paper/2201.01357/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2022.

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

- [Image-based Treatment Effect Heterogeneity](https://mlsanity.com/paper/2206.06417/) (2022)
- [Doubly Robust Fusion of Many Treatments for Policy Learning](https://mlsanity.com/paper/2505.08092/) (2025)
- [Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis](https://mlsanity.com/paper/2201.08343/) (2022)
- [Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design](https://mlsanity.com/paper/2310.12026/) (2023)
- [Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash](https://mlsanity.com/paper/2311.14698/) (2023)
- [Qini Curve Estimation under Clustered Network Interference](https://mlsanity.com/paper/2502.20097/) (2025)
- [Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach](https://mlsanity.com/paper/2601.00287/) (2026)

## Abstract (arXiv)

Estimation of heterogeneous treatment effects is an active area of research. Most of the existing methods, however, focus on estimating the conditional average treatment effects of a single, binary treatment given a set of pre-treatment covariates. In this paper, we propose a method to estimate the heterogeneous causal effects of high-dimensional treatments, which poses unique challenges in terms of estimation and interpretation. The proposed approach finds maximally heterogeneous groups and uses a Bayesian mixture of regularized logistic regressions to identify groups of units who exhibit similar patterns of treatment effects. By directly modeling group membership with covariates, the proposed methodology allows one to explore the unit characteristics that are associated with different patterns of treatment effects. Our motivating application is conjoint analysis, which is a popular type of survey experiment in social science and marketing research and is based on a high-dimensional factorial design. We apply the proposed methodology to the conjoint data, where survey respondents are asked to select one of two immigrant profiles with randomly selected attributes. We find that a group of respondents with a relatively high degree of prejudice appears to discriminate against immigrants from non-European countries like Iraq. An open-source software package is available for implementing the proposed methodology.