Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long
Follow-up Time
- URL: http://arxiv.org/abs/2109.11929v1
- Date: Mon, 20 Sep 2021 13:21:39 GMT
- Title: Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long
Follow-up Time
- Authors: Adi Lin and Jie Lu and Junyu Xuan and Fujin Zhu and Guangquan Zhang
- Abstract summary: Causal effect estimation for dynamic treatment regimes (DTRs) contributes to sequential decision making.
We combine outcome regression models with treatment models for high dimensional features using uncensored subjects that are small in sample size.
Also, the developed deep Bayesian models can model uncertainty and output the prediction variance which is essential for the safety-aware applications, such as self-driving cars and medical treatment design.
- Score: 28.11470886127216
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Causal effect estimation for dynamic treatment regimes (DTRs) contributes to
sequential decision making. However, censoring and time-dependent confounding
under DTRs are challenging as the amount of observational data declines over
time due to a reducing sample size but the feature dimension increases over
time. Long-term follow-up compounds these challenges. Another challenge is the
highly complex relationships between confounders, treatments, and outcomes,
which causes the traditional and commonly used linear methods to fail. We
combine outcome regression models with treatment models for high dimensional
features using uncensored subjects that are small in sample size and we fit
deep Bayesian models for outcome regression models to reveal the complex
relationships between confounders, treatments, and outcomes. Also, the
developed deep Bayesian models can model uncertainty and output the prediction
variance which is essential for the safety-aware applications, such as
self-driving cars and medical treatment design. The experimental results on
medical simulations of HIV treatment show the ability of the proposed method to
obtain stable and accurate dynamic causal effect estimation from observational
data, especially with long-term follow-up. Our technique provides practical
guidance for sequential decision making, and policy-making.
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