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, refutation and evaluation, and policy optimization. We introduce practical contributions not currently available in existing causal ML toolkits, specifically: (1) a method for detecting and quantifying positivity violations in continuous treatment settings (2) a novel, scalable two-stage dimensionality reduction framework tailored for causal inference with high-dimensional data; (3) the adaptation...
| Authors | Javier Moral Hernández, Clara Higuera-Cabañes, Álvaro Ibraín |
|---|---|
| Primary category | stat.ME |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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