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.

翻译：异质性处理效应的估计是当前研究的热点领域。然而，现有方法大多聚焦于在给定一组预处理协变量的情况下，估计单一二元处理的条件下平均处理效应。本文提出一种估计高维处理异质性因果效应的方法，这在估计和解释方面均带来了独特的挑战。所提出的方法寻找最大异质性分组，并利用正则化逻辑回归的贝叶斯混合模型来识别呈现相似处理效应模式的单元组。通过直接以协变量建模组别成员关系，该方法使得研究者能够探究与不同处理效应模式相关联的单元特征。我们的驱动性应用是联合分析——一种基于高维析因设计、在社会科学与市场研究中广泛使用的调查实验。我们将所提出的方法应用于联合数据，其中调查受访者被要求从两个具有随机选择属性的移民档案中选择其一。研究发现，一个具有相对较高偏见程度的受访者群体似乎对来自伊拉克等非欧洲国家的移民存在歧视倾向。已有开源软件包可用于实现所提出的方法。