Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments
Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments
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DOI：
10.1093/pan/mpt024
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发表时间：
2014-12-01
影响因子：
5.4
通讯作者：
Yamamoto, Teppei
中科院分区：
文献类型：
--
作者：
Hainmueller, Jens;Hopkins, Daniel J.;Yamamoto, Teppei
调查实验是因果推理的核心工具。然而，经典的调查实验的设计，使他们无法确定哪些组件的多维治疗是有影响力的。在这里，我们将展示如何联合分析，实验设计尚未被广泛应用于政治学，使研究人员能够估计多个治疗成分的因果效应，并同时评估几个因果假设。在联合分析中，受访者对一组选项进行评分，其中每个选项具有随机变化的属性。在这里，我们进行了正式的识别分析，将联合分析与因果推理的潜在结果框架相结合。我们提出了一个新的因果估计，并表明它可以非参数识别，很容易估计使用完全随机设计的联合数据。分析使我们能够提出诊断检查的识别假设。然后，我们证明了这些技术的价值，通过实证应用选民的决策和对移民的态度。
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.