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08/31/2023
A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents
Global sustainability requires low-carbon urban transport systems, shape...
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06/22/2023
Causal discovery for time series from multiple datasets with latent contexts
Causal discovery from time series data is a typical problem setting acro...
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06/20/2023
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
Conditional independence (CI) testing is frequently used in data analysi...
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06/15/2023
Bootstrap aggregation and confidence measures to improve time series causal discovery
Causal discovery methods have demonstrated the ability to identify the t...
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06/12/2023
Foundations of Causal Discovery on Groups of Variables
Discovering causal relationships from observational data is a challengin...
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05/21/2023
Discovering Causal Relations and Equations from Data
Physics is a field of science that has traditionally used the scientific...
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05/19/2023
Formalising causal inference in time and frequency on process graphs with latent components
When dealing with time series data, causal inference methods often emplo...
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04/11/2023
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery
Robust feature selection is vital for creating reliable and interpretabl...
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09/28/2022
Vector causal inference between two groups of variables
Methods to identify cause-effect relationships currently mostly assume t...
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05/30/2022
Causal inference for temporal patterns
Complex dynamical systems are prevalent in many scientific disciplines. ...
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04/16/2021
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
Satellite images are snapshots of the Earth surface. We propose to forec...
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03/10/2021
Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing
Bias in classifiers is a severe issue of modern deep learning methods, e...
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02/20/2021
Necessary and sufficient conditions for optimal adjustment sets in causal graphical models with hidden variables
The problem of selecting optimal valid backdoor adjustment sets to estim...
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12/11/2020
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
Climate change is global, yet its concrete impacts can strongly vary bet...
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07/03/2020
High-recall causal discovery for autocorrelated time series with latent confounders
We present a new method for linear and nonlinear, lagged and contemporan...
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07/02/2020
A Perspective on Gaussian Processes for Earth Observation
Earth observation (EO) by airborne and satellite remote sensing and in-s...
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07/01/2020
Reconstructing regime-dependent causal relationships from observational time series
Inferring causal relations from observational time series data is a key ...
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03/07/2020
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
We consider causal discovery from time series using conditional independ...
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09/05/2017
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Conditional independence testing is a fundamental problem underlying cau...
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06/18/2015
Optimal model-free prediction from multivariate time series
Forecasting a time series from multivariate predictors constitutes a cha...
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10/09/2012