mlsanityData to 14 Oct 2026

Estimating treatment effects from single-arm trials via latent-variable modeling

arXiv 2311.03002 ↗First posted 6 Nov 2023cs.LG stat.MLcited by 1

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Abstract (arXiv)

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 em (i) patient matching if treatment outcomes are not available for the treatment group, or for em (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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 36 of its 88 references that are in the map. First posted on arXiv 6 Nov 2023.

Builds on

36 of 88 references.

References · 36

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24 more
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