Causal discovery using model invariance through knockoff interventions

Deep‐learning based causal inference: A feasibility study based on three years of tectonic‐climate data from Moxa geodynamic observatory

W Ahmad, V Kasburg, N Kukowski… - Earth and Space …, 2024 - Wiley Online Library

Highly sensitive laser strainmeters at Moxa Geodynamic Observatory (MGO) measure
motions of the upper Earth's crust. Since the mountain overburden of the laser strainmeters …

Embracing the black box: Heading towards foundation models for causal discovery from time series data

G Stein, M Shadaydeh, J Denzler - arXiv preprint arXiv:2402.09305, 2024 - arxiv.org

Causal discovery from time series data encompasses many existing solutions, including
those based on deep learning techniques. However, these methods typically do not endorse …

Deep Learning-based Group Causal Inference in Multivariate Time-series

W Ahmad, M Shadaydeh, J Denzler - arXiv preprint arXiv:2401.08386, 2024 - arxiv.org

Causal inference in a nonlinear system of multivariate timeseries is instrumental in
disentangling the intricate web of relationships among variables, enabling us to make more …

被引用次数：1 相关文章所有 3 个版本

[PDF] arxiv.org

Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions

VT Trifunov, M Shadaydeh, J Denzler - arXiv preprint arXiv:2209.11497, 2022 - arxiv.org

Latent variables often mask cause-effect relationships in observational data which provokes
spurious links that may be misinterpreted as causal. This problem sparks great interest in the …

被引用次数：1 相关文章所有 3 个版本

[PDF] arxiv.org

Regime Identification for Improving Causal Analysis in Non-stationary Timeseries

W Ahmad, M Shadaydeh, J Denzler - arXiv preprint arXiv:2405.02315, 2024 - arxiv.org

Time series data from real-world systems often display non-stationary behavior, indicating
varying statistical characteristics over time. This inherent variability poses significant …