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Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint
When Do Traditional and Causal Decomposition Methods Dive...
[Submitted on 23 Jun 2025 (v1), last revised 23 Jun 2026 (this v · 2026-06-25 · via stat updates on arXiv.org

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Abstract:Identifying malleable factors that can reduce social disparities is a central objective among researchers across disciplines. Traditionally, researchers have relied on the difference-in-coefficients and Kitagawa-Oaxaca-Blinder frameworks. More recently, methods grounded in the potential outcomes framework have emerged. While these methods share the same goal of identifying drivers of disparity, they frequently yield divergent results depending on the underlying confounding structures and research settings. Despite these significant differences, applied researchers lack clear guidance on selecting appropriate methods for their specific research contexts. To address this gap, this study provides a systematic review and offer an intuitive guidance through Directed Acyclic Graphs and comparative simulation studies. We begin by reviewing each method assuming no unmeasured confounding, which is often violated in observational settings. Consequently, we extend our analysis to two realistic scenarios: 1) unmeasured confounding exists in the relationship between intermediate confounders and the mediator, and 2) unmeasured confounding exists in the relationship between the mediator and the outcome. Finally, we illustrate these recommendations through a case study examining the role of educational attainment in explaining racial disparities in later-life cognition.

Submission history

From: Soojin Park [view email]
[v1] Mon, 23 Jun 2025 19:08:18 UTC (693 KB)
[v2] Mon, 4 Aug 2025 16:55:18 UTC (711 KB)
[v3] Tue, 23 Jun 2026 19:39:32 UTC (1,385 KB)