Replication data for: Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision-making and attitudes toward immigrants.

调查实验是因果推断（causal inference）领域的核心研究工具。然而，传统调查实验的设计局限使其无法识别多维干预（multidimensional treatment）的哪些组分具备因果影响力。本文表明，联合分析（conjoint analysis）——一种尚未在政治学领域得到广泛应用的实验设计——可帮助研究者同时估算多个干预组分的因果效应并检验多项因果假设。在联合分析实验中，受访者需对一系列备选方案进行评分，每个备选方案的属性均为随机设定的变体。本文通过正式的识别分析，将联合分析与因果推断的潜在结果框架（potential outcomes framework）相结合。我们提出了一种全新的因果估计量（causal estimand），并证明在完全随机化实验设计下，该估计量可通过非参数方法实现识别，且可轻松从联合分析数据中完成估算。该分析框架还支持我们提出针对识别假设的诊断检验方法。最后，我们通过将这些方法应用于选民决策与民众对移民态度的两个实证场景，验证了其应用价值。