CAUSALRLSTACK: adaptive balancing of deep representation and causal effect estimation with application to HIV-related health data.

BACKGROUND AND

objectiveEstimating individualized causal effects plays a vital role in data-driven decision-making, especially in high-risk domains such as public health. However, current causal inference models often lack flexibility and generalizability due to the tight coupling between representation learning and effect estimation. This study aims to develop a modular and adaptive framework to enhance the analysis of individualized causal effects in complex health data.

methodsWe propose CAUSALRLSTACK, a modular framework designed to separate representation learning from causal effect estimation. In practice, the model uses a memory-augmented Transformer (TITAN) to capture complex, individualized representations. It is further paired with a doubly robust estimator(DRLearner) to improve the treatment effect estimation. A reinforcement learning agent adjusts how much each component contributes by assigning instance-specific weights. This adaptive weighting process improves the model's ability to generalize across different populations. Input features are derived from causal graphs, automatically chosen between an expert-defined graph and one discovered from data. To evaluate performance, we applied the framework to two publicly available HIV datasets that reflect community-level testing behavior and post-intervention clinical outcomes.

resultsCAUSALRLSTACK outperforms six state-of-the-art causal inference models across both datasets, achieving the highest accuracy (0.861 and 0.855), F1-Score (0.845 and 0.839), and AUC-ROC (0.897 and 0.892). It also achieves the lowest predictive uncertainty (0.093 and 0.092), indicating robust performance in estimating treatment effects.

conclusionsThe proposed framework offers a flexible and effective solution for individualized causal inference. Its modular architecture and reinforcement learning-based weighting strategy enable adaptive, data-driven estimation across diverse populations. Strong experimental results demonstrate the potential of the framework to advance individualized causal inference in health data and provide a practical basis for designing personalized intervention strategies in HIV and broader public health domains.