Estimating treatment effects from single-arm trials via latent-variable modeling
Loading...
Access rights
openAccess
CC BY
CC BY
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, Volume 238, pp. 2926-2934, Proceedings of Machine Learning Research ; Volume 238
Abstract
Randomized controlled trials (RCTs) are the accepted standard for treatment effect estimation but they can be infeasible due to ethical reasons and prohibitive costs. Single-arm trials, where all patients belong to the treatment group, can be a viable alternative but require access to an external control group. We propose an identifiable deep latent-variable model for this scenario that can also account for missing covariate observations by modeling their structured missingness patterns. Our method uses amortized variational inference to learn both group-specific and identifiable shared latent representations, which can subsequently be used for (i) patient matching if treatment outcomes are not available for the treatment group, or for (ii) direct treatment effect estimation assuming outcomes are available for both groups. We evaluate the model on a public benchmark as well as on a data set consisting of a published RCT study and real-world electronic health records. Compared to previous methods, our results show improved performance both for direct treatment effect estimation as well as for effect estimation via patient matching.Description
Keywords
Other note
Citation
Haussmann, M, Le, T M S, Halla-aho, V, Kurki, S, Leinonen, J, Koskinen, M, Kaski, S & Lähdesmäki, H 2024, Estimating treatment effects from single-arm trials via latent-variable modeling. in Proceedings of the 27th International Conference on Artificial Intelligence and Statistics . vol. 238, Proceedings of Machine Learning Research, vol. 238, JMLR, pp. 2926-2934, International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 02/05/2024. < https://arxiv.org/abs/2311.03002 >