Counterfactual Policy Learning for Personalized Treatment Using SGLT2 Inhibitors
- 1Budapest University of Technology and Economics
Abstract
BackgroundPersonalized medicine aims to tailor treatments to individual patients, but its implementation is limited by scarce clinical resources and heterogeneous treatment effects. Electronic health records now offer longitudinal data that can support data-driven therapy selection. We focus on SGLT2 inhibitors--medications with broad metabolic and cardiovascular benefits--whose clinical impact varies significantly based on individual patient profiles.
MethodsWe propose a counterfactual Structural Causal Model (SCM) to estimate individual treatment effects (ITE) of SGLT2 inhibitors from observational data. The model assumes counterfactual stability and uses a Gumbel-Max sampling mechanism to capture stochastic treatment assignment while enabling differentiable inference. Optimal treatments are defined by maximizing the counterfactual value of future diagnostic outcomes.
ResultsWe evaluate the Counterfactual Gumbel-Max SCM using a doubly robust value estimator and show that, across diagnostic categories, it outperforms real-world prescriptions in predicted treatment value, highlighting subpopulations where SGLT2 inhibitors yield superior outcomes.
ConclusionBy integrating causal inference with electronic health data, this framework provides individualized counterfactual reasoning about SGLT2 treatment effects and can support clinicians in identifying optimal therapies for type 2 diabetes and comorbid conditions.
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BibTeX
@misc{sandor2026counterfactual,
title = {{Counterfactual Policy Learning for Personalized Treatment Using SGLT2 Inhibitors}},
author = {D. Sandor and A. Nemeth and B. Daroczy and F. Sztanek and M. Harangi and P. Antal},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.15.26365465},
url = {https://www.medrxiv.org/content/10.64898/2026.10.15.26365465v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Counterfactual Policy Learning for Personalized Treatment Using SGLT2 Inhibitors
AU - D. Sandor
AU - A. Nemeth
AU - B. Daroczy
AU - F. Sztanek
AU - M. Harangi
AU - P. Antal
PY - 2026
DA - 2026/10/15
PB - medRxiv
DO - 10.64898/2026.10.15.26365465
UR - https://www.medrxiv.org/content/10.64898/2026.10.15.26365465v1
AB - BackgroundPersonalized medicine aims to tailor treatments to individual patients, but its implementation is limited by scarce clinical resources and heterogeneous treatment effects. Electronic health records now offer longitudinal data that can support data-driven therapy selection. We focus on SGLT2 inhibitors--medications with broad metabolic and cardiovascular benefits--whose clinical impact varies significantly based on individual patient profiles. MethodsWe propose a counterfactual Structural Causal Model (SCM) to estimate individual treatment effects (ITE) of SGLT2 inhibitors from observational data. The model assumes counterfactual stability and uses a Gumbel-Max sampling mechanism to capture stochastic treatment assignment while enabling differentiable inference. Optimal treatments are defined by maximizing the counterfactual value of future diagnostic outcomes. ResultsWe evaluate the Counterfactual Gumbel-Max SCM using a doubly robust value estimator and show that, across diagnostic categories, it outperforms real-world prescriptions in predicted treatment value, highlighting subpopulations where SGLT2 inhibitors yield superior outcomes. ConclusionBy integrating causal inference with electronic health data, this framework provides individualized counterfactual reasoning about SGLT2 treatment effects and can support clinicians in identifying optimal therapies for type 2 diabetes and comorbid conditions.
ER -