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

TL;DR

This paper introduces a deep Bayesian approach for estimating causal effects in dynamic treatment regimes with long follow-up periods, addressing challenges like censoring, confounding, and complex relationships.

Contribution

It combines outcome and treatment models with deep Bayesian methods to handle high-dimensional data and model uncertainty, improving long-term causal effect estimation.

Findings

Achieves stable and accurate causal effect estimates in HIV treatment simulations.

Handles high-dimensional, complex relationships better than traditional linear methods.

Provides uncertainty quantification for safer decision-making in medical and autonomous systems.

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.