arXiv:2606. 08941v1 Announce Type: cross Abstract: This paper proposes a collapsible method for estimating causal effects that maintains the estimator's consistency before and after marginalization over some variables in completed partially directed acyclic graphs (CPDAGs).
arXiv:2609.23535v1 Announce Type: new Abstract: Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on...
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:2606. 08196v1 Announce Type: cross Abstract: We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM).
The paper addresses causal discovery in Directed Acyclic Graphs where nodes are either ordinal (modeled with an ordered logit) or follow a one‑parameter exponential family distribution. It proves that the direction of edges between such nodes is identifiable for generic parameter values, extending prior Ordinal‑Poisson results. The authors also propose a score‑based exhaustive search and a masked continuous optimization method using DAGMA, and demonstrate through simulations that these approaches recover orientations that are otherwise unidentifiable under classical structural equation models.
arXiv:2503. 08245v4 Announce Type: replace Abstract: In mixed graphs, there are both directed and bidirected edges.
arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.
The paper presents a method for partially identifying counterfactual queries without requiring a fully specified causal graph. By exploiting the topological ordering implied by the query itself, the authors transform the identification problem into a linear programming task, enabling bounds on arbitrary counterfactual and nested counterfactual queries. They demonstrate the tightness of these bounds and illustrate the approach on several case studies, showing its usefulness even when causal knowledge is incomplete.
arXiv:2601. 16249v3 Announce Type: replace-cross Abstract: Learning DAG structures from purely observational data remains a long-standing challenge across scientific domains.
arXiv:2505. 15215v3 Announce Type: replace-cross Abstract: Data fusion, the process of combining observational and experimental data, can enable the identification of causal effects that would otherwise remain non-identifiable.
arXiv:2609.27256v1 Announce Type: cross Abstract: We study causal discovery where each node is a random function. Previous studies on this topic rely on structural assumptions, e.g., linearity or non...
arXiv:2603.24333v2 Announce Type: replace-cross Abstract: Recently, Forr\'e (arXiv:2104.11547, 2021) introduced transitional conditional independence, a notion of conditional independence that provid...