[Estimation on the individual treatment effect among heterogeneous population, using the Causal Forests method]

Objective: This project aimed to explore the effectiveness of estimating individual treatment effect on real data, among the heterogeneous population, with Causal Forests (CF) method, to find out the characteristics of heterogeneous population. Methods: We designed and conducted four computer simulation schemes to verify the effect of estimating on individual treatment, using the CF under four different environments of the treatment effects. Real data was then analyzed for the catheterization on right heart. Results: Results from the simulation process showed that the values on individual treatment effect that were estimated by causal forests were consistent with the population effect as well as in line with the expected distribution under the setting of four different effect values. Results of real data analysis showed that values of individual treatment effect among most patients appeared positive, so the use of RHC could cause an increase of the '180-day mortality rate' in the sampled population. Patients with lower predicted probability of 2-mo survival and albumin were more likely to have a lower risk of death after using the RHC. Conclusion: CF method could be effectively used to estimate the individual treatment effect and helping the individuals to make decision on the receipt of treatment.