D'ya like dags? a survey on structure learning and causal discovery
Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …
causal relationships from data, we need structure discovery methods. We provide a review …
Recent developments in empirical dynamic modelling
Ecosystems are complex and sparsely observed making inference and prediction
challenging. Empirical dynamic modelling (EDM) circumvents the need for a parametric …
challenging. Empirical dynamic modelling (EDM) circumvents the need for a parametric …
Causal networks of phytoplankton diversity and biomass are modulated by environmental context
Untangling causal links and feedbacks among biodiversity, ecosystem functioning, and
environmental factors is challenging due to their complex and context-dependent …
environmental factors is challenging due to their complex and context-dependent …
Data-driven causal analysis of observational biological time series
Complex systems are challenging to understand, especially when they defy manipulative
experiments for practical or ethical reasons. Several fields have developed parallel …
experiments for practical or ethical reasons. Several fields have developed parallel …
Comparison of six methods for the detection of causality in a bivariate time series
A Krakovská, J Jakubík, M Chvosteková, D Coufal… - Physical Review E, 2018 - APS
In this comparative study, six causality detection methods were compared, namely, the
Granger vector autoregressive test, the extended Granger test, the kernel version of the …
Granger vector autoregressive test, the extended Granger test, the kernel version of the …
Causal inference from noisy time-series data—Testing the Convergent Cross-Mapping algorithm in the presence of noise and external influence
Abstract Convergent Cross-Mapping (CCM) has shown high potential to perform causal
inference in the absence of detailed models. This has implications for the understanding of …
inference in the absence of detailed models. This has implications for the understanding of …
Topological causality in dynamical systems
D Harnack, E Laminski, M Schünemann, KR Pawelzik - Physical review letters, 2017 - APS
Determination of causal relations among observables is of fundamental interest in many
fields dealing with complex systems. Since nonlinear systems generically behave as …
fields dealing with complex systems. Since nonlinear systems generically behave as …
A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series
W Ren, B Li, M Han - Physica A: Statistical Mechanics and its Applications, 2020 - Elsevier
The causality analysis is an important research topic in time series data mining. Granger
causality analysis is a powerful method that determines cause and effect based on …
causality analysis is a powerful method that determines cause and effect based on …
Convergent cross sorting for estimating dynamic coupling
Natural systems exhibit diverse behavior generated by complex interactions between their
constituent parts. To characterize these interactions, we introduce Convergent Cross Sorting …
constituent parts. To characterize these interactions, we introduce Convergent Cross Sorting …
Usefulness and limitations of convergent cross sorting and continuity scaling methods for their application in simulated and real-world time series
AD Bahamonde, RM Montes… - Royal Society Open …, 2023 - royalsocietypublishing.org
Causality detection methods are valuable tools for detecting causal links in complex
systems. The efficiency of continuity scaling (CS) and the convergent cross sorting (CSS) …
systems. The efficiency of continuity scaling (CS) and the convergent cross sorting (CSS) …