Article
Version 1
Preserved in Portico This version is not peer-reviewed
Enhancing Precision: Unveiling Individualized Treatment Effects with Advanced Computational Methods
Version 1
: Received: 28 April 2024 / Approved: 29 April 2024 / Online: 29 April 2024 (10:34:18 CEST)
How to cite: Mandavalli, S. Enhancing Precision: Unveiling Individualized Treatment Effects with Advanced Computational Methods. Preprints 2024, 2024041875. https://doi.org/10.20944/preprints202404.1875.v1 Mandavalli, S. Enhancing Precision: Unveiling Individualized Treatment Effects with Advanced Computational Methods. Preprints 2024, 2024041875. https://doi.org/10.20944/preprints202404.1875.v1
Abstract
In areas like commercial and policy decisions—it's important to know how different treatments affect things and how well they work. However, treatments affect things on average—but now—with fancy computer methods and big sets of data, interest lies in figuring out how treatments might work differently for different people. For instance, let's say instead of just looking at what works for most people, researchers want to know which treatment is best for each person. This would help doctors to decide the right treatment for each patient. This idea isn't just for healthcare—it can help in other areas too, like deciding how to govern employees or making government policies. There are some ways people have been trying to do this already, but they struggle when there are lots of different factors to consider. New computer methods, though, are good at handling lots of factors, but they need a ton of data to work well. Therefore, this study looks at three computer methods that are—Virtual Twin Random Forest (VTRF), Causal Forest (CF), and Causal Net (CN) using real-world data about encouraging people to do manual labour. One problem this study is trying to solve is something called the "winner's curse", where a treatment might seem better than it really is because of how it's predicted by the computer. We are also testing some ideas to see if we can fix this problem and make the predictions more accurate.
Keywords
Causal Forest; Causal Net; Healthcare; Virtual Twin Random Forest
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment