An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making
An end-to-end causal machine learning pipeline designed for real-world applications with continuous treatments that offers a more computationally efficient approach and produces less biased estimates compared to standard methods for problems with continuous treatment and high-dimensional data is presented.
Javier Moral, Clara Higuera-Cabañes, Álvaro Ibrain
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