arXiv Machine Learning By Gijs van Seeventer, Saber Salehkaleybar

Sign Identifiability of Causal Effects in Stationary Stochastic Dynamical Systems

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arXiv:2603. 08311v2 Announce Type: replace-cross Abstract: We study identifiability in continuous-time linear stationary stochastic differential equations with a known causal structure.

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arXiv Machine Learning
Jun 29

Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts

arXiv:2606. 28228v1 Announce Type: new Abstract: Causal representation learning for time series has developed strong identifiability results in discrete-time latent causal models, but identifiability in continuous-time latent stochastic differential equation (SDE) models remains largely open.

By Yuanyuan Wang, Wenjie Wang, Haoxuan Li, Mingming Gong, Kun Zhang
arXiv AI
Jun 19

Computational Identifiability

arXiv:2606. 19361v1 Announce Type: cross Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available.

By Lucius E. J. Bynum, Rajesh Ranganath, Kyunghyun Cho
arXiv Machine Learning
Sep 18

Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms

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.

By Sambit Mishra, Yingying Wang, Christine K. Johnson, Urbashi Mitra
arXiv AI
Jul 16

Partially Observed Structural Causal Models

arXiv:2605. 03268v2 Announce Type: replace-cross Abstract: Here we introduce Partially Observed Structural Causal Models (POSCMs) as an extension of structural causal models (SCMs) to settings where upstream contexts co-determine both the interaction structure and downstream mechanisms on observed variables.

By Turan Orujlu, Jordan Matelsky, Martin V. Butz, Charley M. Wu, Konrad P. Kording
arXiv AI
Jun 24

Infinitesimal Causality

arXiv:2606. 24621v1 Announce Type: cross Abstract: This paper introduces a categorical account of infinitesimal causality in Frobenius Markov categories equipped with tangent-bundle semantics.

By Sridhar Mahadevan