Pipeline improves causal analysis with continuous actions in finance
An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making
Machine Learning
Summary
Understanding how different levels of an action affect outcomes is hard, especially when many factors are involved. This paper presents a complete system to study cause and effect when the actions can take on a range of values, not just yes or no. The authors created new steps to simplify data, check for problems, and make sure the analysis is reliable. They tested their approach on made-up financial data and found it is faster and more accurate than standard methods. The system’s code is shared openly for others to use and adapt.
What this means in practice
- •For financial analytics teams: Design and evaluate policies with continuous financial interventions while addressing data complexity and bias in causal estimates.
- •For marketing analysts: Analyze the effect of varying marketing spend levels on customer response more accurately under complex data conditions.
Authors
Javier Moral Hernández, Clara Higuera-Cabañes, Álvaro Ibraín
Abstract
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 of sensitivity analysis and estimation methods originally designed for binary treatments to the continuous treatment space and (4) an end-to-end integration of these components into a modular, reproducible workflow. These innovations address real-world challenges in causal inference that are often not covered in theoretical frameworks but frequently encountered in industrial applications. The methodology is validated with a synthetic dataset inspired in a real-world financial debt collection use case, however its design can be applied to analogous problems across different industries. Results demonstrate that the proposed methodology offers a more computationally efficient approach and produces less biased estimates compared to standard methods for problems with continuous treatment and high-dimensional data. A fully functional GitHub repository with documented code and numbered notebooks is made available ensuring reproducibility and practical implementation. The pipeline presented is intended to contribute to closing the gap between academic approaches and practical application in industry contexts where causal ML can be highly beneficial such as the financial sector.