Summary
The field of causal inference has a clear need for methodologies that can effectively uncover heterogeneous effects in continuous treatment/exposure settings while producing interpretable results. To address this gap, we propose a novel approach called the Continuous Causal Interaction Tree (CCIT). The CCIT framework is designed to accommodate various exposure-response estimation methods, providing flexibility in its implementation and high interpretability in its output. By leveraging the CCIT method, researchers can gain valuable insights into the complex dynamics of continuous exposure scenarios. CCIT serves as a powerful tool for discovering and understanding heterogeneous effects, enabling the generation of interpretable results that contribute to a deeper comprehension of causal relationships.