|
|
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. 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. Goplerud, Max, Kosuke Imai, Nicole E. Pashley. (2025). ``Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis.'' Annals of Applied Statistics, Vol. 19, No. 2 (June), pp. 866-888. Ham, Dae Woong, Kosuke Imai, and Lucas Janson. (2024). ``Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis.'' Political Analysis, Vol. 32, No. 3 (July), pp. 329-344. |
You may be
interested in the following software, which implements the proposed
method: ``FindIt: Finding
Heterogeneous Treatment Effects.'' available through
The Comprehensive R
Archive Network. 2015. |