de la Cuesta, Brandon, Naoki Egami, and Kosuke Imai. (2022). ``Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution.'' Political Analysis, Vol. 30, No. 1 (January), pp. 19-45.

Conjoint analysis has become popular among social scientists for measuring multidimensional preferences. When analyzing such experiments, researchers often focus on the average marginal component effect (AMCE), which represents the causal effect of a single profile attribute while averaging over the remaining attributes. What has been overlooked, however, is the fact that the AMCE critically relies upon the distribution of the other attributes used for the averaging. Although most experiments employ the uniform distribution, which equally weights each profile, both the actual distribution of profiles in the real world and the distribution of theoretical interest are often far from uniform. This mismatch can severely compromise the external validity of conjoint analysis. We empirically demonstrate that estimates of the AMCE can be substantially different when averaging over the target profile distribution instead of uniform. We propose new experimental designs and estimation methods that incorporate substantive knowledge about the profile distribution. We illustrate our methodology through two empirical applications, one using a real-world distribution and the other based on a counterfactual distribution motivated by a theoretical consideration. The proposed methodology is implemented through an open-source software package.

Egami, Naoki, Brandon de la Cuesta, and Kosuke Imai. ``factorEx: Design and Analysis for Factorial Experiments.'' available through The Comprehensive R Archive Network and GitHub.

Egami, Naoki, and Kosuke Imai. (2019). ``Causal Interaction in Factorial Experiments: Application to Conjoint Analysis.'' Journal of the American Statistical Association, Vol. 114, No. 526 (June), pp. 529-540.

Goplerud, Max, Kosuke Imai, Nicole E. Pashley. ``Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis.''

Ham, Dae Woong, Kosuke Imai, and Lucas Janson. ``Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis.'' Political Analysis, Forthcoming.