Beyond Propensity Score: The SAHR, Subgroup-Aware Holistic Ridge for Causal Inference in Observational Studies

Authors

https://doi.org/10.22105/raise.v3i2.97

Abstract

Causal inference is critical for decision-making in healthcare, economics, and policy, but it faces challenges such as confounding, heterogeneous treatment effects, and scalability in high-dimensional settings. Traditional methods like propensity scores often struggle with these complexities, particularly when treatment effects vary across subpopulations or datasets are large. To address these limitations, we propose Subgroup-Aware Holistic Ridge (SAHR), a novel framework that utilize subgroup analysis, kernel approximation, and adaptive covariate balancing based on BNN or regression covariates for robust causal inference. SAHR identifies subgroups with heterogeneous treatment effects using Gaussian Mixture Models (GMM), ensures scalability via the Nyström method, and balances covariates adaptively through a Holistic ridge regression framework. Experiments on benchmark datasets demonstrate that SAHR outperforms state-of-the-art methods in predictive accuracy, subgroup identification, and covariate balancing. By addressing key challenges in observational studies, SAHR advances causal inference methodology, offering an interpretable tool for applications in personalized medicine, and policy evaluation.

Keywords:

Causal inference, Subgroup analysis, Covariate balancing, Gaussian mixture models, BNN, Propensity scores, Regression

References

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Published

2026-06-09

Issue

Vol. 3 No. 2 (2026): Research Annals of Industrial and Systems Engineering

Section

Articles

How to Cite

Hamisheh Bahar, M. . (2026). Beyond Propensity Score: The SAHR, Subgroup-Aware Holistic Ridge for Causal Inference in Observational Studies. Research Annals of Industrial and Systems Engineering, 3(2), 134-145. https://doi.org/10.22105/raise.v3i2.97

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