An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making

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, ref