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 method is implemented through the open-source software.
The video of presentation at the Experiments in Governance and Politics Conference is available at here.
You may also be interested in the following articles on heterogenous treatment effects:
Imai, Kosuke and Aaron Strauss. (2011). ``Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-out-the-vote Campaign..'' Political Analysis, Vol. 19, No. 1 (Winter), pp. 1-19.
Imai, Kosuke and Marc Ratkovic (2013). ``Estimating Treatment Effect Heterogeneity in Randomized Program Evaluation..'' Annals of Applied Statistics, Vol. 7, No. 1, pp. 443-470.
© Kosuke Imai