Analysis of Heterogeneity in Digital Economy Policy Effects Using XGBoost-Causal Tree
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Abstract
Addressing the limitations of traditional policy impact evaluation methods in accurately capturing the heterogeneous characteristics of digital economy policies while accounting for sample selection bias and complex causal relationships, this paper constructs an XGBoost-Causal Tree integrated analytical framework to systematically investigate policy effect differentiation characteristics. As digital economy development increasingly relies on advanced communication infrastructure and electromagnetic information transmission technologies, accurately evaluating policy heterogeneity has become essential for supporting intelligent industrial transformation and digital connectivity. First, drawing upon policy evaluation theory, machine learning, and causal inference theory, the core dimensions and evaluation metrics for policy effect differentiation are established. Second, based on panel data from 30 Chinese provinces spanning 2011–2022, a multidimensional dataset (DEP-2024) incorporating economic foundations, industrial structure, digital infrastructure, and related factors is constructed. Third, a three-step analytical framework consisting of Propensity Score Matching, XGBoost feature selection, and Causal Tree heterogeneity decomposition is designed to identify key influencing factors and quantify differentiated policy effects across heterogeneous groups. Finally, policy heterogeneity is validated from the perspectives of regional development, industrial structure, and digital infrastructure, and targeted optimization strategies are proposed. The proposed framework provides a reliable data-driven approach for policy assessment and offers valuable insights for digital infrastructure planning and intelligent resource allocation in future electromagnetic information and communication environments.
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