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:对不同治疗效果的估算是因果推断研究的一个积极领域。但是,大多数现有方法侧重于估计单一二进制治疗的有条件平均治疗效果,并配有一套预处理共变法。在本文件中,我们提议了一种方法来估计高维治疗的不同因果效应,这在估计和解释方面构成独特的挑战。拟议方法基于一种巴伊西亚的正规化后勤回归组合,以确定具有类似治疗效果的单位组别。通过直接模拟同系物的集群成员,拟议方法允许人们探索与不同治疗效果模式相关的单位特征。我们的激励应用是共合分析,这是社会科学和营销研究的流行调查实验,以高维因子设计为基础。我们将拟议方法应用于同系数据,要求调查对象选择具有随机选择属性的两个移民特征的移民特征之一。我们发现,一组具有相对高度偏见的受访者似乎歧视来自伊拉克等非欧洲国家的移民。一个开放源软件包可用于实施拟议方法。