A quantile-adaptive distributional Granger causal model (QA-DGCM) is developed to infer non-causal paths via an m-stage hypothesis testings based on distributional Granger causal effect (DGCE) statistic, which yields unique neurobiological insights advancing whole-brain effective connectivity analysis.
Understanding the propagation of extreme events is important in many economic and environmental applications, yet most econometric methods for causal inference focus on average effects rather than tail behavior. This paper studies the identification of causal relations in extremes and derives resulting estimators and t...
The Causal Regime Bayesian (CaReBayes) forecasting framework is proposed, which integrates regime detection, causal discovery, and Bayesian forecasting within a unified approach and produces regime-dependent causal graphs that summarize candidate structural relationships in the system, enhancing interpretability.
We propose a framework for estimating conditional extreme quantile treatment effects (CEQTEs) in observational studies with heavy-tailed outcomes. Our procedure first estimates intermediate conditional quantiles using inverse-probability-weighted (IPW) quantile regression and then extrapolates them to extreme levels us...
Xiao-Rui Wang, Juan Cai, H. J. Wang et al.· 0 citations
Causal questions have long been central to psychological research, particularly in randomized experiments, while formal causal-inference methods are increasingly being applied to observational and quasi-experimental data. Common outcome-regression and propensity-score approaches can be sensitive to nuisance-model missp...
This work proposes SURE-Ridge, a non-iterative, closed-form estimator for equal variance linear Gaussian SEM, which achieves the lowest structural Hamming distance in the small-sample regime and the lowest run time across all sample sizes tested, compared with NOTEARS, DAGMA, and GBNSL baselines.
Constraint-based causal discovery like PC and FCI depends on its conditional independence test. Partial correlation and the Generalised Covariance Measure (GCM) detect only the conditional covariance of residuals, so they miss dependence in the mean's nonlinear part, the scale, and the tails. Tests that detect more are...
Pavel Averin, Theodoros Moysiadis, Ioannis Katakis· 0 citations
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