方法对比

	Conjoint Survey Experiment	联合分析
领域	Political Science	实验设计
方法族	Process / pipeline	Hypothesis test
起源年份	2014	1978
提出者	Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto	Paul E. Green & V. Srinivasan
类型	Multi-attribute forced-choice survey experiment with design-based causal estimands	Decomposition-based utility estimation
开创性文献	Hainmueller, J., Hopkins, D. J., & Yamamoto, T. (2014). Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments. Political Analysis, 22(1), 1–30.	Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.
别名	Causal conjoint, Forced-choice conjoint experiment, AMCE conjoint, Conjoint experiment	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint
相关	4	6
摘要	A conjoint survey experiment presents respondents with profiles — of candidates, immigrants, policies, or products — described by several attributes whose levels are independently randomized, and asks respondents to choose between or rate the profiles. Hainmueller, Hopkins, and Yamamoto's 2014 framework places this design on a rigorous causal footing, defining the average marginal component effect (AMCE) as the design-based causal effect of an attribute level, averaged over the randomization distribution of all other attributes. It lets political scientists estimate the relative causal weight of many decision factors simultaneously from realistic, multidimensional choices.	Conjoint analysis is a preference-measurement technique that decomposes overall product evaluations into the separate utility values — called part-worths — that respondents assign to each attribute level. Formalised by Green and Srinivasan in their seminal 1978 Journal of Consumer Research paper, the method has become the dominant tool in marketing research and product design for quantifying what buyers truly trade off when they choose between options.