Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Time

09/20/2021
by   Adi Lin, et al.
4

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.

READ FULL TEXT
research
07/30/2021

Semiparametric Estimation of Long-Term Treatment Effects

This paper studies the estimation of long-term treatment effects though ...
research
02/15/2022

Long-term Causal Inference Under Persistent Confounding via Data Combination

We study the identification and estimation of long-term treatment effect...
research
06/07/2021

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation

Proxy causal learning (PCL) is a method for estimating the causal effect...
research
01/22/2020

Cohort state-transition models in R: From conceptualization to implementation

Decision models can synthesize evidence from different sources to provid...
research
02/25/2019

SMARTp: A SMART design for non-surgical treatments of chronic periodontitis with spatially-referenced and non-randomly missing skewed outcomes

This paper proposes dynamic treatment regimes for choosing individualize...
research
02/14/2018

Using Longitudinal Targeted Maximum Likelihood Estimation in Complex Settings with Dynamic Interventions

Longitudinal targeted maximum likelihood estimation (LTMLE) has hardly e...
research
03/23/2020

G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes

Counterfactual prediction is a fundamental task in decision-making. G-co...

Please sign up or login with your details

Forgot password? Click here to reset