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Deep representation learning for continuous treatment effect estimation

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2023-09-25
Authors/Contributors
Abstract
Estimating the individual treatment effect (ITE) from observational datasets has important applications in domains such as personalized medicine, economics, and recommendation systems. The observational datasets often exhibit treatment-selection bias, resulting in a distribution shift among populations of samples that received different treatments. While deep representation learning has shown great promise in adjusting for covariate shifts when the treatment is a binary variable, the more practical and challenging task of handling continuous treatments (e.g., dosage of a medication) remains relatively underexplored. In this thesis, our aim is to address the associated challenges with continuous treatment. Specifically, we propose a deep model that mitigates the distribution shift through an adversarial procedure and predicts the potential outcomes using an attention mechanism. The model's objective is grounded in a theoretical upper bound on counterfactual prediction error. Our experimental evaluation on semi-synthetic datasets also demonstrates the method's empirical superiority over a range of state-of-the-art.
Document
Extent
34 pages.
Identifier
etd22764
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Ester, Martin
Language
English
Member of collection
Download file Size
etd22764.pdf 1.27 MB

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