Machine learning models are increasingly used to explore survival and treatment outcomes for stroke patients. This study addresses that gap by applying causal inference methods to estimate the survival impact of different stroke treatment strategies by using real-world clinical data. The investigation was conducted by analysing records from 944 stroke patients treated at the Clinical Centre of Montenegro. The data variables included demographic characteristics, clinical status, stroke type, and treatment information. The proposed method addressed the observed problem of high deviation and differences in sensitivity across medical treatment types within patient groups with the same stroke type diagnosis. Propensity Score Matching was used to construct comparable patient groups based on demographic and medical data and to reduce bias inherent in observational data. The causal forests machine learning method was applied to explore causal relationships and estimate individual patterns that affect survival and recovery across patient subgroups. The applied method revealed age, gender, and health status effects, highlighting differences in patient responses to treatment, as compared to the comparative evaluation without group matching and causal effect research.