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

By Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto. Political Analysis, 2013

1,787 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Political Science and International Relations, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6210bf55cf40a85be1dc284a0e88da762b88d9af

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Citations in the corpus

Which Clients are Deserving of Help? : A Theoretical Model and Experimental Test (2018, background): “The order of student attributes was randomized across respondents to avoid order effects, but fixed within respondents to avoid confusion (see also Hainmueller et al. 2014).”
Factors Associated With US Adults’ Likelihood of Accepting COVID-19 Vaccination (2020, background): “Educational attainment Less than high school 34 (2) High school/GED 410 (21) Some college 583 (30) 4-y College degree 469 (24) Graduate school 475 (24)”
The Ties That Double Bind: Social Roles and Women's Underrepresentation in Politics (2018, positive): “Following Hainmueller, Hopkins, and Yamamoto (2014), we use cluster-robust standard errors at the respondent level to correct for potential withinrespondent clustering.”
What Have We Learned about Gender from Candidate Choice Experiments? A Meta-Analysis of Sixty-Seven Factorial Survey Experiments (2021, methodology): “When subjects rate multiple candidate pro ﬁ les, we follow standard practice in the analysis of conjoint experiments and cluster our standard errors by re-spondent (Hainmueller et al. 2014).”
Combining climate, economic, and social policy builds public support for climate action in the US (2020, methodology): “The researcher can then calculate Average Marginal Component-Specific Effects (AMCEs) for each element of each policy bundle (Hainmueller et al 2014).”
The Contingent Effects of Candidate Sex on Voter Choice (2019, methodology): “…vary all treatments simultaneously to allow for assessment of the impact of the independent and interactive effects of multiple variables on a common outcome metric without sacrificing much statistical power or imposing assumptions about functional form (Hainmueller, Hopkins, and Yamamoto 2014).”
Causal Interaction in Factorial Experiments: Application to Conjoint Analysis (2018, neutral): “Although the uniform or empirical distribution would be a reasonable default choice for many experimentalists, researchers can improve the external validity of their exper-iment by using a treatment distribution based on the target population (Hainmueller, Hopkins, and Yamamoto 2014).”
Discovery of Treatments from Text Corpora (2016, methodology): “In this paper we focus on estimating the Average Marginal Component Speciﬁc Effect for factor k (AMCE k ) (Hainmueller et al., 2014).”
Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution (2021, neutral): “, 2001; Marshall and Bradlow, 2002), conjoint analysis has quickly gained popularity in political science due to its wide applicability and relative simplicity (Hainmueller et al., 2014).”