Estimation of heterogeneous treatment effects is an active area of research in causal inference. 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 is based on a Bayesian mixture of regularized logistic regressions to identify groups of units who exhibit similar patterns of treatment effects. By directly modeling cluster 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 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因果推断领域的研究热点。然而，现有方法大多聚焦于在给定预处理协变量条件下，估计单一二元处理变量的条件平均处理效应。本文提出一种针对高维处理变量异质性因果效应的估计方法，这在估计与解释层面均构成独特挑战。该方法基于贝叶斯正则化逻辑回归混合模型，识别呈现相似处理效应模式的单元组群。通过利用协变量直接建模组群归属，所提方法能够探索与不同处理效应模式相关的单元特征。本文的应用场景为联合分析——社会科学与市场研究领域广泛采用的调查实验方法，其核心是基于高维析因设计。我们将该方法应用于联合数据（受访者需在具有随机属性的两个移民档案中择一），发现具有较高偏见程度的受访者群体倾向于歧视来自非欧洲国家（如伊拉克）的移民。该方法已开发为开源软件包供实施。