Skip to main content Accessibility help

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

  • Jens Hainmueller (a1), Daniel J. Hopkins (a2) and Teppei Yamamoto (a3)


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.

    • Send article to Kindle

      To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

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

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

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

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

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


Corresponding author

e-mail: (corresponding author)


Hide All

Authors' note: We gratefully acknowledge the recommendations of Political Analysis editors Michael Alvarez and Jonathan Katz as well as the anonymous reviewers. We further thank Justin Grimmer, Kosuke Imai, and seminar participants at MIT, Harvard University, Georgetown University, and Rochester University for their helpful comments and suggestions. We are also grateful to Anton Strezhnev for excellent research assistance. An earlier version of this article was presented at the 2012 Annual Summer Meeting of the Society for Political Methodology and the 2013 Annual Meeting of the American Political Science Association. Example scripts that illustrate the estimators and companion software to embed a conjoint analysis in Web-based survey instruments are available on the authors' websites. Replication materials are available online as Hainmueller, Hopkins, and Yamamoto (2013). Supplementary materials for this article are available on the Political Analysis Web site.



Hide All
Alexander, C. S., and Becker, H. J. 1978. The use of vignettes in survey research. Public Opinion Quarterly 42(1): 93104.
Alves, W. M., and Rossi, P. H. 1978. Who should get what? Fairness judgments of the distribution of earnings. American Journal of Sociology 84(3): 541–64.
Barabas, J., and Jerit, J. 2010. Are survey experiments externally valid? American Political Science Review 104(2): 226–42.
Bechtel, M., Hainmueller, J., and Margalit, Y. 2013. Studying public opinion on multidimensional policies: The case of the Eurozone bailouts. MIT Political Science Department Paper.
Bechtel, M., and Scheve, K. 2013. Public support for global climate cooperation. Mimeo, Stanford University.
Berinsky, A. J., Huber, G. A., and Lenz, G. S. 2012. Evaluating online labor markets for experimental research:'s Mechanical Turk. Political Analysis 20: 351–68.
Bettman, J. R., Luce, M. F., and Payne, J. W. 1998. Constructive consumer choice processes. Journal of Consumer Research 25(3): 187217.
Brader, T., Valentino, N., and Suhay, E. 2008. Is it immigration or the immigrants? The emotional influence of groups on public opinion and political action. American Journal of Political Science 52(4): 959–78.
Campbell, A., Converse, P. E., Miller, W. E., and Stokes, D. E. 1960. The American voter. New York: Wiley.
Citrin, J., Green, D. P., Muste, C., and Wong, C. 1997. Public opinion toward immigration reform: The role of economic motivations. Journal of Politics 59(3): 858–81.
Cutler, F. 2002. The simplest shortcut of all: Sociodemographic characteristics and electoral choice. Journal of Politics 64(2): 466–90.
Diamond, P. A., and Hausman, J. A. 1994. Contingent valuation: Is some number better than no number? Journal of Economic Perspectives 8(4): 4564.
Faia, M. A. 1980. The vagaries of the vignette world: A comment on Alves and Rossi. American Journal of Sociology 85(4): 951–4.
Gaines, B. J., Kuklinski, J. H., and Quirk, P. J. 2007. The logic of the survey experiment re-examined. Political Analysis 15(1): 120.
Gerber, A. S., and Green, D. P. 2012. Field experiments: design, analysis, and interpretation. New York: W. W. Norton & Company.
Green, P. E., Krieger, A. M., and Wind, Y. J. 2001. Thirty years of conjoint analysis: Reflections and prospects. Interfaces 31: 5673.
Green, D. P., Palmquist, B., and Schickler, E. 2002. Partisan hearts and minds. New Haven, CT: Yale University Press.
Green, P. E., and Rao, V. R. 1971. Conjoint measurement for quantifying judgmental data. Journal of Marketing Research 8: 355–63.
Hainmueller, J., and Hiscox, M. J. 2010. Attitudes toward highly skilled and low-skilled immigration: Evidence from a survey experiment. American Political Science Review 104(1): 6184.
Hainmueller, J., and Hopkins, D. J. 2012. The hidden American immigration consensus: A conjoint analysis of attitudes toward immigrants. SSRN Working Paper.
Hainmueller, J., Hopkins, D. J., and Yamamoto, T. 2013. Replication data for: Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments. hdl:1902.1/22603. The Dataverse Network.
Hauser, J. R. 2007. A note on conjoint analysis. MIT Sloan Courseware, Massachusetts Institute of Technology.
Hedström, P., and Ylikoski, P. 2010. Causal mechanisms in the social sciences. Annual Review of Sociology 36: 4967.
Holland, P. W. 1986. Statistics and causal inference. Journal of the American Statistical Association 81: 945–60.
Imai, K., Keele, L., Tingley, D., and Yamamoto, T. 2011. Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. American Political Science Review 105(4): 765–89.
Jasso, G., and Rossi, P. H. 1977. Distributive justice and earned income. American Sociological Review 42(4): 639–51.
Luce, R. D., and Tukey, J. W. 1964. Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology 1(1): 127.
Malhotra, N. K. 1982. Information load and consumer decision making. Journal of Consumer Research 8: 1930.
McFadden, D. L. 1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in econometrics, ed. Zarembka, P., 105–42. New York: Academic Press.
Mendelberg, T. 2001. The race card: Campaign strategy, implicit messages, and the norm of equality. Princeton, NJ: Princeton University Press.
Neyman, J. 1923. On the application of probability theory to agricultural experiments: Essay on principles, section 9. (translated in 1990). Statistical Science 5: 465–80.
Raghavarao, D., Wiley, J. B., and Chitturi, P. 2011. Choice-based conjoint analysis: Models and designs. Boca Raton, FL: CRC Press.
Rossi, P. H. 1979. Vignette analysis: Uncovering the normative structure of complex judgments. In Qualitative and Quantitative Social Research: Papers in Honor of Paul F. Lazarsfeld, eds. Merton, Robert K., et al. New York: Free Press.
Rossi, P. H., and Alves, W. M. 1980. Rejoinder to Faia. American Journal of Sociology 85(4): 954–5.
Rubin, D. B. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66(5): 688701.
Rubin, D. B. 1980. Comments on “Randomization analysis of experimental data: The Fisher randomization test” by D. Basu. Journal of the American Statistical Association 75: 591–93.
Sawtooth Software Inc. 2008. The CBC system for choice-based conjoint analysis. Sawtooth Software Technical Paper Series. (accessed December 6, 2013).
Scheve, K., and Slaughter, M. 2001. Labor market competition and individual preferences over immigration policy. Review of Economics and Statistics 83(1): 133–45.
Schildkraut, D. 2011. Americanism in the twenty-first century: Public opinion in the age of immigration. New York: Cambridge University Press.
Schulte, A. 2002. Consensus versus disagreement in disease-related stigma: A comparison of reactions to AIDS and cancer patients. Sociological Perspectives 45(1): 81104.
Schuman, H., and Bobo, L. 1988. Survey-based experiments on white racial attitudes toward residential integration. American Journal of Sociology 94: 273–99.
Sniderman, P. M. 2011. The logic and design of the survey experiment. In Cambridge Handbook of Experimental Political Science, eds. Druckman, James et al. New York: Cambridge University Press.
Sniderman, P., and Carmines, E. 1997. Reaching beyond race. Cambridge, MA: Harvard University Press.
Sniderman, P. M., and Grob, D. B. 1996. Innovations in experimental design in attitude surveys. Annual Review of Sociology 22: 377–99.
Strezhnev, A., Hainmueller, J., Hopkins, D. J., and Yamamoto, T. 2013. Conjoint SDT. (accessed December 6, 2013).
VanderWeele, T. J., and Robins, J. M. 2009. Minimal sufficient causation and directed acyclic graphs. Annals of Statistics 37(3): 1437–465.
Verlegh, P. W., Schifferstein, H. N., and Wittink, D. R. 2002. Range and number-of-levels effects in derived and stated measures of attribute importance. Marketing Letters 13(1): 4152.
Wallander, L. 2009. 25 years of factorial surveys in sociology: A review. Social Science Research 38: 505–20.
Wong, C. J. 2010. Boundaries of obligation in American politics: Geographic, national, and racial communities. New York: Cambridge University Press.
Wright, M., and Citrin, J. 2011. Saved by the stars and stripes? Images of protest, salience of threat, and immigration attitudes. American Politics Research 39(2): 323–43.
Wright, M., Levy, M., and Citrin, J. 2013. Who should be allowed to stay? American public opinion on legal status for illegal immigrants. Working paper, American University.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
Type Description Title
Supplementary materials

Hainmueller et al. supplementary material
Supplemental Information

 PDF (242 KB)
242 KB


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed