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

• URL: http://arxiv.org/abs/2507.15899v1
• Date: Mon, 21 Jul 2025 03:57:42 GMT
• Title: Structural DID with ML: Theory, Simulation, and a Roadmap for Applied Research
• Abstract summary: Causal inference in observational panel data has become a central concern in economics,policy analysis, and the broader social sciences.<n>This paper proposes an innovative framework called S-DID that integrates structural identification with high-dimensional estimation.
• Score: 3.0031348283981987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal inference in observational panel data has become a central concern in economics,policy analysis,and the broader social sciences.To address 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-dimensional estimation.Building upon 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 interference issues.It designs a dynamic heterogeneity estimation module combining causal forests and semi-parametric models to capture spatiotemporal heterogeneity effects.The framework establishes a complete modular application process with standardized Stata implementation paths.The introduction of S-DIDML enriches methodological research on DID and DDML innovations, shifting causal inference from method stacking to architecture integration.This advancement enables social sciences to precisely identify policy-sensitive groups and optimize resource allocation.The framework provides replicable evaluation tools, decision optimization references,and methodological paradigms for complex intervention scenarios such as digital transformation policies and environmental regulations.

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