The paper introduces COARSE, a score‑based method for learning cluster directed acyclic graphs (DAGs) from interventional data. COARSE retains the two‑phase structure of prior constraint‑based approaches—first identifying clusters, then edges—but replaces the edge‑learning phase with a score‑based local search under a cluster‑level BIC score, assuming linear Gaussian relationships. The authors prove polynomial‑time complexity and consistency (given correct intervention targets) and demonstrate that COARSE achieves comparable edge recovery to state‑of‑the‑art methods while being up to two orders of magnitude faster, even on dense graphs with hundreds of nodes.
The paper introduces Cluster-DAGs as a flexible prior knowledge framework to improve causal discovery. It presents two modified constraint‑based algorithms, Cluster‑PC and Cluster‑FCI, tailored for fully and partially observed data. Experiments on simulated data show that these methods outperform baseline algorithms that lack prior knowledge.
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.
arXiv:2607. 03364v1 Announce Type: cross Abstract: We propose \textbf{CaSPECT}, a causal spectral clustering framework for discovering causally homogeneous subgroups from observational data.
arXiv:2604. 22416v2 Announce Type: replace-cross Abstract: Latent variables pose a fundamental obstacle to both causal discovery and inference.
arXiv:2607. 08238v1 Announce Type: new Abstract: Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery.
arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.
arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).
arXiv:2608.28991v1 Announce Type: cross Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Exist...
arXiv:2609.30643v1 Announce Type: new Abstract: We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) wit...
arXiv:2510. 22298v2 Announce Type: replace-cross Abstract: Uncovering the causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collection costs and involve unknown interventions.
arXiv:2607. 04447v1 Announce Type: new Abstract: Local causal discovery is a scalable alternative to global structure learning.
The paper evaluates four main approaches to scalable nonlinear causal discovery—differentiable, amortized, score‑matching, and combinatorial search—highlighting their distinct accuracy‑runtime trade‑offs. It introduces SPADE, a spline‑based score‑evaluation method that compiles and reuses sufficient statistics, reducing combinatorial search complexity from O(nd³) to O(nd²+d³). Empirical results show SPADE dramatically extends the scalability‑accuracy frontier, solving large‑scale problems with high structural accuracy on both synthetic and real‑world data.