ESTIMATING HETEROGENEOUS CAUSAL EFFECTS OF HIGH-DIMENSIONAL TREATMENTS: APPLICATION TO CONJOINT ANALYSIS

• 成果类型: Article
• 署名作者: Goplerud, Max; Imai, Kosuke; Pashley, Nicole E.
• 署名单位: University of Texas System; University of Texas Austin; Harvard University; Harvard University; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1994
• 发表日期: 2025
• 页码: 866-888
• 关键词: maximum-likelihood; variable selection; mixture; regression; models; CHOICE

摘要

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 pretreatment 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.