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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Oxford University Press
2014
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| Online Access: | http://hdl.handle.net/1721.1/84064 https://orcid.org/0000-0002-8079-7675 |
| _version_ | 1826203288299634688 |
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| author | Hainmueller, Jens Yamamoto, Teppei Hopkins, Daniel J. |
| author2 | Massachusetts Institute of Technology. Department of Political Science |
| author_facet | Massachusetts Institute of Technology. Department of Political Science Hainmueller, Jens Yamamoto, Teppei Hopkins, Daniel J. |
| author_sort | Hainmueller, Jens |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:34:16Z |
| format | Article |
| id | mit-1721.1/84064 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:34:16Z |
| publishDate | 2014 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | mit-1721.1/840642022-09-28T08:41:12Z Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments Hainmueller, Jens Yamamoto, Teppei Hopkins, Daniel J. Massachusetts Institute of Technology. Department of Political Science Hainmueller, Jens Yamamoto, Teppei 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. 2014-01-17T16:45:21Z 2014-01-17T16:45:21Z 2013-12 Article http://purl.org/eprint/type/JournalArticle 1047-1987 1476-4989 http://hdl.handle.net/1721.1/84064 Hainmueller, J., D. J. Hopkins, and T. Yamamoto. “Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments.” Political Analysis (December 19, 2013). https://orcid.org/0000-0002-8079-7675 en_US http://dx.doi.org/10.1093/pan/mpt024 Political Analysis Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc/3.0 application/pdf Oxford University Press SSRN |
| spellingShingle | Hainmueller, Jens Yamamoto, Teppei Hopkins, Daniel J. Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments |
| title | Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments |
| title_full | Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments |
| title_fullStr | Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments |
| title_full_unstemmed | Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments |
| title_short | Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments |
| title_sort | causal inference in conjoint analysis understanding multidimensional choices via stated preference experiments |
| url | http://hdl.handle.net/1721.1/84064 https://orcid.org/0000-0002-8079-7675 |
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