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
• Authors: Yile Yu, Anzhi Xu, Yi Wang,
• 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.

Related papers

• Spatiodynamic inference using vision-based generative modelling [0.5461938536945723]
  We develop a simulation-based inference framework that employs vision transformer-driven encoded variational representations.<n>The central idea is to construct a fine-grained, structured mesh of latent dynamics through systematic exploration of the parameter space.<n>By integrating generative modeling with mechanistic principles, our approach provides a unified inference framework.
  arXiv  Detail & Related papers  (2025-07-29T22:10:50Z)
• CTRLS: Chain-of-Thought Reasoning via Latent State-Transition [57.51370433303236]
  Chain-of-thought (CoT) reasoning enables large language models to break down complex problems into interpretable intermediate steps.<n>We introduce groundingS, a framework that formulates CoT reasoning as a Markov decision process (MDP) with latent state transitions.<n>We show improvements in reasoning accuracy, diversity, and exploration efficiency across benchmark reasoning tasks.
  arXiv  Detail & Related papers  (2025-07-10T21:32:18Z)
• Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains [50.66049136093248]
  We develop a time-aware structural causal model (SCM) that incorporates dynamic causal factors and the causal mechanism drifts.<n>We show that our method can yield the optimal causal predictor for each time domain.<n>Results on both synthetic and real-world datasets exhibit that SYNC can achieve superior temporal generalization performance.
  arXiv  Detail & Related papers  (2025-06-21T14:05:37Z)
• Modeling and Visualization Reasoning for Stakeholders in Education and Industry Integration Systems: Research on Structured Synthetic Dialogue Data Generation Based on NIST Standards [3.5516803380598074]
  This study addresses the structural complexity and semantic ambiguity in stakeholder interactions within the Education-Industry Integration (EII) system.<n>We propose a structural modeling paradigm based on the National Institute of Standards and Technology (NIST) synthetic data quality framework.
  arXiv  Detail & Related papers  (2025-06-20T12:37:43Z)
• Aligning MLLM Benchmark With Human Preferences via Structural Equation Modeling [17.092510377905814]
  evaluating multimodal large language models (MLLMs) remains a fundamental challenge due to a lack of structured, interpretable, and theoretically grounded benchmark designs.<n>We propose a novel framework for aligning MLLM benchmark based on Structural Equation Modeling (SEM) to analyze and quantify the internal validity, dimensional separability, and contribution of benchmark components.<n> Experimental results demonstrate that the proposed benchmark exhibits stronger interpretability, reduced indicator redundancy, and clearer cognitive consistency compared to existing approaches.
  arXiv  Detail & Related papers  (2025-06-13T08:04:56Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• Model Hemorrhage and the Robustness Limits of Large Language Models [119.46442117681147]
  Large language models (LLMs) demonstrate strong performance across natural language processing tasks, yet undergo significant performance degradation when modified for deployment.<n>We define this phenomenon as model hemorrhage - performance decline caused by parameter alterations and architectural changes.
  arXiv  Detail & Related papers  (2025-03-31T10:16:03Z)
• Time Series Domain Adaptation via Latent Invariant Causal Mechanism [28.329164754662354]
  Time series domain adaptation aims to transfer the complex temporal dependence from the labeled source domain to the unlabeled target domain.<n>Recent advances leverage the stable causal mechanism over observed variables to model the domain-invariant temporal dependence.<n>However, modeling precise causal structures in high-dimensional data, such as videos, remains challenging.
  arXiv  Detail & Related papers  (2025-02-23T16:25:58Z)
• Generalized Factor Neural Network Model for High-dimensional Regression [50.554377879576066]
  We tackle the challenges of modeling high-dimensional data sets with latent low-dimensional structures hidden within complex, non-linear, and noisy relationships.<n>Our approach enables a seamless integration of concepts from non-parametric regression, factor models, and neural networks for high-dimensional regression.
  arXiv  Detail & Related papers  (2025-02-16T23:13:55Z)
• Interpreting token compositionality in LLMs: A robustness analysis [10.777646083061395]
  Constituent-Aware Pooling (CAP) is a methodology designed to analyse how large language models process linguistic structures.<n>CAP intervenes in model activations through constituent-based pooling at various model levels.<n>Our findings highlight fundamental limitations in current transformer architectures regarding compositional semantics processing and model interpretability.
  arXiv  Detail & Related papers  (2024-10-16T18:10:50Z)
• Persistent Topological Features in Large Language Models [0.6597195879147556]
  We introduce topological descriptors that measure how topological features, $p$-dimensional holes, persist and evolve throughout the layers.<n>This offers a statistical perspective on how prompts are rearranged and their relative positions changed in the representation space.<n>As a showcase application, we use zigzag persistence to establish a criterion for layer pruning, achieving results comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-14T19:46:23Z)
• Images in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputs [77.54052164713394]
  This paper explores the intersection of Discrete Choice Modeling (DCM) and machine learning. We investigate the consequences of embedding high-dimensional image data that shares isomorphic information with traditional tabular inputs within a DCM framework.
  arXiv  Detail & Related papers  (2023-12-22T14:33:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.