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AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery
Marco Ruiz, Miguel Arana-Catania, David R. Ardila +1
Environmental time-series causal discovery requires expert decisions about method choice, conditional-independence tests, lag horizons, sample-size adequacy, multiple-testing contr…
Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Marco Ruiz, Miguel Arana-Catania, David R. Ardila +1
Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure lea…
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction
İrem Üstek, Miguel Arana-Catania, Alexander Farr +1
Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wil…
Wind Estimation in Unmanned Aerial Vehicles with Causal Machine Learning
Abdulaziz Alwalan, Miguel Arana-Catania
In this work we demonstrate the possibility of estimating the wind environment of a UAV without specialised sensors, using only the UAV's trajectory, applying a causal machine lear…