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.
目的： 探讨因果森林在异质性人群中估计个体处理效应的有效性及如何应用于实例数据以挖掘异质性人群特征。 方法： 设计4种模拟方案，通过模拟试验验证因果森林在不同处理效应环境设置下对个体处理效应进行估计的效果，并应用于右心导管插入术实例数据集进行分析。 结果： 模拟试验结果表明，在4种不同效应值设置下，用因果森林方法所估计的个体处理效应值都能与总体效应相吻合，符合预期分布；实例数据分析结果显示绝大多数患者个体处理效应为正值，使用RHC会导致该样本人群180 d死亡率增高，2月生存模型估计概率和白蛋白含量偏低的患者在使用RHC后更倾向于有较低的死亡风险。 结论： 因果森林能够有效地估计个体处理效应，为个体是否接受某种处理提供建议。.
Keywords: Causal forests; Heterogeneous; Individual treatment effect.