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

TL;DR

This paper introduces S-DIDML, a novel framework combining structural causal inference with machine learning to improve high-dimensional panel data analysis in social sciences.

Contribution

It develops an integrated architecture that retains DID structure while addressing high-dimensional confounders using orthogonalization and causal forests.

Findings

Enables precise identification of policy-sensitive groups.

Provides a standardized implementation process in Stata.

Enhances causal inference in complex intervention scenarios.

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.