An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making
连续处理因果机器学习的端到端流程:金融决策应用
This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed framework consists of six sequential steps: dimensionality reduction, causal identification, positivity assumption violation handling, estimation, refutation and evaluation, and policy optimization. We introduce practical contributions not currently available in existing causal ML toolkits, specifically: (1) a method for…