Search Results for author:
Found 5 papers, 2 papers with code
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science.
KGS: Causal Discovery Using Knowledge-guided Greedy Equivalence Search
Prior causal information such as the presence or absence of a causal edge can be leveraged to guide the discovery process towards a more restricted and accurate search space.
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
The ability to understand causality from data is one of the major milestones of human-level intelligence.
eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)
Conventional temporal causal discovery (CD) methods suffer from high dimensionality, fail to identify lagged causal relationships, and often ignore dynamics in relations.
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data
Our proposed method addresses several limitations of existing causal discovery methods for autocorrelated and non-stationary time series data, such as high dimensionality, the inability to identify lagged causal relationships, and overlooking changing modules.
