Basic Theory and Methodology
Estimation on the individual treatment effect among heterogeneous population, using the Causal Forests method
Wenjing He, Dongfang You, Ruyang Zhang, Hao Yu, Feng Chen, Zhibin Hu, Yang Zhao
Published 2019-06-10
Cite as Chin J Epidemiol, 2019, 40(6): 707-712. DOI: 10.3760/cma.j.issn.0254-6450.2019.06.020
Abstract
ObjectiveThis 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.
MethodsWe 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.
ResultsResults 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.
ConclusionCF method could be effectively used to estimate the individual treatment effect and helping the individuals to make decision on the receipt of treatment.
Key words:
Causal forests; Heterogeneous; Individual treatment effect
Contributor Information
Wenjing He
Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
Dongfang You
Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
Key Laboratory of Modern Toxicology, Ministry of Education, Nanjing Medical University, Nanjing 211166, China
Ruyang Zhang
Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
Joint Laboratory of Health and Risk Assessment, School of Public Health, Nanjing Medical University-Harvard University, Nanjing 211166, China
Hao Yu
Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
Key Laboratory of Biomedical Big Data, Nanjing Medical University, Nanjing 211166, China
Feng Chen
Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
Joint Laboratory of Health and Risk Assessment, School of Public Health, Nanjing Medical University-Harvard University, Nanjing 211166, China
Zhibin Hu
Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
Yang Zhao
Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
Jiangsu Provincial Key Laboratory of Malignant Tumor Biomarkers and Prevention, Nanjing 211166, China
Collaborative Innovation Center for Individual Medicine in Cancer, Nanjing 211166, China
