Causal Interaction in Factorial Experiments: Application to Conjoint Analysis
By Naoki Egami, Kosuke Imai. Journal of the American Statistical Association, 2018
117 citations recorded by the source corpus.
ABSTRACT We study causal interaction in factorial experiments, in which several factors, each with multiple levels, are randomized to form a large number of possible treatment combinations. Examples of such experiments include conjoint analysis, which is often used by social scientists to analyze multidimensional preferences in a population. To characterize the structure of causal interaction in factorial experiments, we propose a new causal interaction effect, called the average marginal interaction effect (AMIE). Unlike the conventional interaction effect, the relative magnitude of the AMIE does not depend on the choice of baseline conditions, making its interpretation intuitive even for higher-order interactions. We show that the AMIE can be nonparametrically estimated using ANOVA regression with weighted zero-sum constraints. Because the AMIEs are invariant to the choice of baseline conditions, we directly regularize them by collapsing levels and selecting factors within a penalized ANOVA framework. This regularized estimation procedure reduces false discovery rate and further facilitates interpretation. Finally, we apply the proposed methodology to the conjoint analysis of ethnic voting behavior in Africa and find clear patterns of causal interaction between politicians’ ethnicity and their prior records. The proposed methodology is implemented in an open source software package. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f10cb4c9b67a45dfac7a18cc8bfe5ea3f43ff1d8
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Citations in the corpus
- Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution (2021, unknown): “5 Importantly, we do not shrink the coefficients β`d themselves and instead regularize their differences |β`d ́ β`,d ́1| so that we can collapse unnecessary levels (Egami and Imai, 2019).”
- A Survey on Causal Inference (2020, background): “Studying the causal interaction is a trending topic of high-dimensional treatment, which aims to identify the combinations of treatments that induce large additional effects beyond the sum of effects separately attributable to each treatment [45].”