Primary Submission Category: Matching, Weighting
Causal Interaction and Effect Modification: A Randomization-Based Approach to Inference
Authors: Zion Lee, Kwonsang Lee,
Presenting Author: Zion Lee*
Understanding causal interactions is critical in observational studies but often challenging due to confounding and methodological limitations. While these concepts have been extensively studied in randomized experiments, their application in observational data remains limited, particularly in settings requiring stratification to evaluate interactions. We propose a novel randomization-based inference framework utilizing matching methods to investigate causal interactions. Additionally, we provide a comprehensive review of causal interaction, explaining its unique focus on joint causal effects and distinguishing it from statistical interaction and effect modification. Using a real-world dataset, we analyze the joint effects of two treatments: residential fire safety equipment and fire response time. Our approach demonstrates how matching can mitigate confounding and identify interaction effects, offering a robust alternative to traditional methods. The findings highlight the importance of considering causal interaction in public safety interventions and provide actionable insights for fire safety policy and fire response optimization.