Generalized Random Forest For Predictive Modeling And Causal Inference: A Comparative Study With Traditonal Approaches

This paper provides a theoretical review of predictive modeling by utilizing traditional methodologies and introduces a novel approach, Generalized Random Forest (GRF), as an alternative method that also serves as a complementary tool for causal inference and estimating treatment effects. Conventional techniques such as Multiple Linear Regression (MLR) and Partial Least Squares Structural Equation Modeling (PLS-SEM) are explored in terms of predictive capabilities, causal relationships, and internal features, determining how their underlying mechanisms, use cases, and limitations differ from the GRF method. In order to support the robustness and the validity of the results, the comparison is conducted using three benchmark datasets from the PLS-SEM literature - TAM (Technology Acceptance Model), CUSL (Corporate Reputation Model) and UTAUT (Unified Theory of Acceptance and Use of Technology). The analysis focuses on the potential of GRF to produce results similar to traditional methods, while also providing additional insights through its advanced procedures for modeling data-driven heterogeneity, estimating conditional treatment effects, and refining mechanisms in prediction algorithms. By incorporating features such as heterogeneous treatment effect estimation, non-linear relationship modeling, and adaptive weighting using observation similarity, GRF turns into a valuable tool for informed decision-making based on rich data structures by revealing critical behavioral drivers across customer segments and by designing targeted policies. Thus, GRF holds strong potential in economic research, where understanding heterogeneous responses and complex causal relationships across diverse populations becomes essential.