A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis
In the following short article we adapt a new and popular machine learning model for inference on medical data sets. Our method is based on the Variational AutoEncoder (VAE) framework that we adapt to survival analysis on small data sets with missing values. In our model, the true health status appears as a set of latent variables that affects the observed covariates and the survival chances. We show that this flexible model allows insightful decision-making using a predicted distribution and outperforms a classic survival analysis model.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningDecision MakingMissing ValuesSmall Data Image ClassificationSurvival AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
Estimating causal effects from observational data is challenging, especially in the presence of latent confounders. Much work has been done on addressing this challenge, but most of the existing research ignores the bias…
Deep Learning of Continuous and Structured Policies for Aggregated Heterogeneous Treatment Effects
As estimation of Heterogeneous Treatment Effect (HTE) is increasingly adopted across a wide range of scientific and industrial applications, the treatment action space can naturally expand, from a binary treatment variab…
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
Counterfactual reasoning is an important paradigm applicable in many fields, such as healthcare, economics, and education. In this work, we propose a novel method to address the issue of \textit{selection bias}. We learn…
counterfactualCounterfactual ReasoningSelection biasNonparametric Bounds on Treatment Effects with Imperfect Instruments
This paper extends the identification results in Nevo and Rosen (2012) to nonparametric models. We derive nonparametric bounds on the average treatment effect when an imperfect instrument is available. As in Nevo and Ros…
On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement
Estimating treatment effects from observational data is paramount in healthcare, education, and economics, but current deep disentanglement-based methods to address selection bias are insufficiently handling irrelevant v…
DisentanglementSelection bias