SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification

Area of Science:

• Machine Learning
• Causal Inference
• Health Informatics

Background:

• Accurate treatment effect estimation (TEE) is vital for intervention evaluation.
• Current deep learning models for TEE often assume population homogeneity, limiting personalized treatment recommendations.
• Heterogeneity in treatment effects across subgroups is frequently overlooked.

Purpose of the Study:

• To propose SubgroupTE, a novel model for TEE that incorporates subgroup identification.
• To enhance the precision of treatment effect estimation by accounting for subgroup-specific effects.
• To improve targeted treatment recommendations by identifying patient subgroups with differential responses.

Main Methods:

• Developed SubgroupTE, a deep learning model integrating subgroup identification into TEE.
• Employed an expectation-maximization (EM)-based training process for iterative optimization of estimation and subgrouping networks.
• Validated the model on synthetic, semi-synthetic, and real-world datasets.

Main Results:

• SubgroupTE demonstrated superior performance in treatment effect estimation and subgrouping compared to existing methods on synthetic and semi-synthetic data.
• The model effectively identified heterogeneous subgroups with varying treatment responses.
• Real-world application showed SubgroupTE's capability in enhancing targeted treatment recommendations for opioid use disorder (OUD).

Conclusions:

• SubgroupTE offers a significant advancement in treatment effect estimation by addressing population heterogeneity.
• The model's ability to identify subgroups and estimate specific effects facilitates more personalized and effective treatment strategies.
• SubgroupTE holds promise for improving clinical decision-making and patient outcomes, particularly in complex conditions like OUD.