AdaPS-LiNGAM is a new method for causal discovery in linear non‑Gaussian acyclic models (LiNGAM) that addresses the problem of small sample sizes relative to the number of variables. The approach adapts the predecessor selection process by reconstructing each residual directly from the original observations using a sparsely chosen subset of previously ordered variables, rather than relying on repeated residualization which becomes degenerate when variables outnumber samples. Experiments on synthetic data show that AdaPS‑LiNGAM achieves accurate causal‑structure recovery in sample‑limited settings and its performance degrades more gradually as the sample size decreases.
arXiv:2607. 05984v1 Announce Type: new Abstract: Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem.
Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem. Although LvLiNGAM is identifiable only up to an observational equivalence class, each equivalence class is characterized by a unique sparsest DAG.
arXiv:2402.03941v4 Announce Type: replace-cross Abstract: Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress...
The article discusses recent advances in learning directed acyclic graphs (DAGs) from observational data, highlighting the challenges of enforcing acyclicity and dealing with latent confounding or heterogeneous noise. It reviews the evolution from combinatorial search methods to continuous, score‑based approaches that use smooth acyclicity constraints, and introduces concomitant DAG estimation techniques that jointly infer sparse causal structure and exogenous noise levels to improve robustness under heteroscedasticity and distribution shifts. The tutorial also outlines future research directions such as online, nonlinear, and neural causal discovery.
The paper introduces Structural Causal Bottleneck Models (SCBMs), a new class of structural causal models where high‑dimensional causal effects are mediated by low‑dimensional bottleneck variables. SCBMs allow mechanism‑specific, target‑dependent dimensionality reduction while remaining estimable with standard learning algorithms. The authors prove that bottleneck variables are identifiable up to bijection from observational data, and demonstrate experimentally that using bottlenecks improves identification and finite‑sample efficiency in causal effect estimation for instrumental variables, mediation, and backdoor adjustment.
arXiv:2609.23516v1 Announce Type: new Abstract: Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become m...
arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.
arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.
arXiv:2606. 18011v1 Announce Type: cross Abstract: Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships.
Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression proposes Loscar, an extension of the RESIT framework to location-scale noise models (LSNMs). Loscar estimates conditional mean and scale using quantile regression, allowing pretrained tabular foundation models to serve as plug‑in estimators. Experiments on bivariate and multivariate causal‑discovery benchmarks show that in‑context quantile regression outperforms a feasible generalized least squares (FGLS) variant, especially when causes are multivariate.
arXiv:2610.28134v1 Announce Type: cross Abstract: Causal discovery with latent confounding is particularly challenging, but a common setting in real-world data. In this work, we focus on linear causa...
arXiv:2609. 18535v1 Announce Type: new Abstract: Causal discovery aims to recover causal relationships from observed data.