articles

In the context of rapid advancements in machine learning and causal inference methodologies, their integration into medical research is of paramount importance. Implementing appropriate methods in the medical domain facilitates robust assessment of treatment efficacy at the individual level. This study aims to conduct experiments on synthetic data and evaluate the accuracy of predicting individual treatment effects using T-learner and S-learner methods. The article presents an integrated approach to medical data analysis, combining causal inference techniques with machine learning algorithms. For the first time, a comprehensive comparison of the effectiveness of T-learner and S-learner methods in assessing individual treatment effects has been conducted. Based on simulated data, the study experimentally determines the optimal application conditions for these methods, depending on the characteristics of clinical data. The experiments revealed that the T-learner method demonstrated higher accuracy (87%) compared to the S-learner (84%), making it preferable when there are significant differences between treatment and control groups. However, the S-learner method exhibited greater generalization capability in scenarios with limited data volume. The c-for-benefit index was employed to validate the predicted treatment effects, with results confirming the high accuracy of both methods. These findings underscore the potential of integrating machine learning and causal inference methods to develop personalized therapeutic strategies and automate medical data analysis, thereby improving clinical outcomes and treatment quality. The developed approach enhances the precision of predicting treatment outcomes at the individual level and can be integrated into clinical decision support systems. The presented results offer new opportunities for personalized healthcare and can serve as a foundation for subsequent research in this field.