Estimating treatment effects from single-arm trials via latent-variable modeling
arxiv(2023)
摘要
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.
更多查看译文
关键词
treatment effects,trials,single-arm,latent-variable
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要