arXiv Machine Learning June 24, 2026 By Mohammad Fesanghary, Abhinav Havaldar

GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series

Read the original on arXiv Machine Learning →

arXiv:2606. 23880v1 Announce Type: new Abstract: From climate teleconnections to gene regulation, modern time-series datasets encompass tens or hundreds of interacting variables, making causal discovery increasingly challenging.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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FoundCause: Causal Discovery with Latent Confounders from Observational Data

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.

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Optimal Experiments for Partial Causal Effect Identification

The paper tackles selecting a cost‑constrained set of experiments that most effectively tighten bounds on a partially identifiable causal query. It formalizes this as the NP‑hard max‑potency problem, introduces efficient graphical pruning rules to reduce the search space, and demonstrates the approach on synthetic graphs and real NHANES data to estimate the effect of physical activity on diabetes.

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FoundCause: Causal Discovery with Latent Confounders from Observational Data

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Optimal Experiments for Partial Causal Effect Identification

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