Identifying causal directions among variables via data-driven approaches is a research hotspot. Researchers now focus on detecting causal direction heterogeneity among multiple variables (variables more than two) when covariates cause such heterogeneity. This study combines the structural equation likelihood function (SELF) method with a recursive partitioning method to achieve an interpretable model of multivariate causal direction heterogeneity in multivariable settings. Through simulation, we compared the performance of the SELF-Tree model in terms of the identification about heterogeneous causal direction under different conditions. Using a public drug consumption dataset, we demonstrated its real data application. The SELF-Tree model offers researchers a new way to understand variable causal direction heterogeneity.
