Structural DID with ML: Theory, Simulation, and a Roadmap for Applied Research

Causal inference in observational panel data has become a central concern in economics,policy analysis,and the broader socialthis http URLaddress the core contradiction where traditional difference-in-differences (DID) struggles with high-dimensional confounding variables in observational panel data,while machine learning (ML) lacks causal structure interpretability,this paper proposes an innovative framework called S-DIDML that integrates structural identification with high-dimensionalthis http URLupon the structure of traditional DID methods,S-DIDML employs structured residual orthogonalization techniques (Neyman orthogonality+cross-fitting) to retain the group-time treatment effect (ATT) identification structure while resolving high-dimensional covariate interferencethis http URLdesigns a dynamic heterogeneity estimation module combining causal forests and semi-parametric models to capture spatiotemporal heterogeneitythis http URLframework establishes a complete modular application process with standardized Stata implementationthis http URLintroduction of S-DIDML enriches methodological research on DID and DDML innovations, shifting causal inference from method stacking to architecturethis http URLadvancement enables social sciences to precisely identify policy-sensitive groups and optimize resourcethis http URLframework provides replicable evaluation tools, decision optimization references,and methodological paradigms for complex intervention scenarios such as digital transformation policies and environmental regulations.