Causal Interaction in High Dimension

• Activity Number: 469
• Type: Invited
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Social Statistics Section
• Author(s): Kosuke Imai* and Naoki Egami
• Companies: Princeton University and The University of Tokyo
• Keywords: factorial treatments ; heterogenous treatment effects ; lasso ; causal inference ; variable selection ; conjoint analysis

Abstract

Estimating causal interaction effects is essential for the exploration of heterogeneous treatment effects. In the presence of multiple treatment variables with each having several levels, researchers are often interested in identifying the combinations of treatments that induce large additional causal effects beyond the sum of separate effects attributable to each treatment. We show, however, the standard approach to causal interaction suffers from the lack of invariance to the choice of baseline condition and the difficulty of interpretation beyond two-way interaction. We propose an alternative definition of causal interaction effect, called the marginal treatment interaction effect, whose relative magnitude does not depend on the choice of baseline condition while maintaining an intuitive interpretation even for higher-order interaction. Our approach enables researchers to effectively summarize the structure of causal interaction in high-dimension by decomposing the total effect of any treatment combination into the marginal effects and the interaction effects. Our motivating example is conjoint analysis where the literature largely assumes the absence of causal interaction.