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Estimation of heterogeneous treatment effects is an active area of researchin causal inference. Most of the existing methods, however, focus on estimatingthe conditional average treatment effects of a single, binary treatment given aset of pre-treatment covariates. In this paper, we propose a method to estimatethe heterogeneous causal effects of high-dimensional treatments, which posesunique challenges in terms of estimation and interpretation. The proposedapproach is based on a Bayesian mixture of regularized logistic regressions toidentify groups of units who exhibit similar patterns of treatment effects. Bydirectly modeling cluster membership with covariates, the proposed methodologyallows one to explore the unit characteristics that are associated withdifferent patterns of treatment effects. Our motivating application is conjointanalysis, which is a popular survey experiment in social science and marketingresearch and is based on a high-dimensional factorial design. We apply theproposed methodology to the conjoint data, where survey respondents are askedto select one of two immigrant profiles with randomly selected attributes. Wefind that a group of respondents with a relatively high degree of prejudiceappears to discriminate against immigrants from non-European countries likeIraq. An open-source software package is available for implementing theproposed methodology.

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