Mandavalli, S. Enhancing Precision: Unveiling Individualized Treatment Effects with Advanced Computational Methods. Preprints2024, 2024041875. https://doi.org/10.20944/preprints202404.1875.v1
APA Style
Mandavalli, S. (2024). Enhancing Precision: Unveiling Individualized Treatment Effects with Advanced Computational Methods. Preprints. https://doi.org/10.20944/preprints202404.1875.v1
Chicago/Turabian Style
Mandavalli, S. 2024 "Enhancing Precision: Unveiling Individualized Treatment Effects with Advanced Computational Methods" Preprints. https://doi.org/10.20944/preprints202404.1875.v1
Abstract
In areas like commercial and policy decisions—it's important to know how different treatments affectthings and how well they work. However, treatments affect things on average—but now—withfancy computer methods and big sets of data, interest lies in figuring out how treatments might work differently fordifferent people. For instance, let's say instead of just looking at what works for most people, researchers want toknow 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 makinggovernment policies. There are some ways people have been trying to do this already, but they struggle when thereare lots of different factors to consider. New computer methods, though, are good at handling lots of factors, but theyneedatonofdatatoworkwell. Therefore, thisstudylooksatthreecomputermethodsthatare—VirtualTwinRandomForest (VTRF),CausalForest (CF),andCausalNet (CN)usingreal-worlddata aboutencouragingpeople todomanuallabour.Oneproblem this study is trying to solve is something called the "winner's curse", where a treatment might seem betterthan it really is because of how it's predicted by the computer. We are also testing some ideas to see if we can fixthis problemand makethepredictionsmoreaccurate.
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.
