Replication Data for: How to detect heterogeneity in conjoint experiments

Conjoint experiments are fast becoming one of the dominant experimental methods within the social sciences. Despite recent efforts to model heterogeneity within this type of experiment, the relationship between the conjoint design and lower-level causal estimands is underdeveloped. In this paper, we clarify how conjoint heterogeneity can be construed as a set of nested, causal parameters that correspond to the levels of the conjoint design. We then use this framework to propose a new estimation strategy, using machine learning, that better allows researchers to evaluate treatment effect heterogeneity. We also provide novel tools for classifying and analysing heterogeneity post-estimation using partitioning algorithms. Replicating two conjoint experiments, we demonstrate our theoretical argument, and show how this method helps estimate and detect substantive patterns of heterogeneity. To accompany this paper, we provide new a R package, cjbart, that allows researchers to model heterogeneity in their experimental conjoint data.

联合实验（Conjoint experiments）正迅速成为社会科学领域的主流实验方法之一。尽管近期已有研究尝试对这类实验中的异质性进行建模，但联合实验设计与低层级因果估计量（causal estimands）之间的关联尚未得到充分研究。本文中，我们阐释了如何将联合实验异质性诠释为一组嵌套式因果参数，这些参数与联合实验设计的各个层级相对应。随后我们依托这一分析框架，提出了一种基于机器学习的全新估计策略，能够更好地帮助研究者评估处理效应异质性（treatment effect heterogeneity）。此外，我们还提供了新颖的工具集，可借助分区算法（partitioning algorithms）在估计完成后对异质性进行分类与分析。通过复现两项联合实验，我们验证了本文的理论论点，并展示了该方法如何助力估计与探测实质性异质性模式。为配合本文研究，我们推出了一款全新的R语言工具包cjbart，可帮助研究者对其联合实验数据中的异质性进行建模。