
Amortized Causal Discovery: Learning to Infer Causal Graphs from TimeSeries Data
Standard causal discovery methods must fit a new model whenever they enc...
read it

Spectral Ranking of Causal Influence in Complex Systems
Like natural complex systems such as the Earth's climate or a living cel...
read it

Quantifying Causal Coupling Strength: A Lagspecific Measure For Multivariate Time Series Related To Transfer Entropy
While it is an important problem to identify the existence of causal ass...
read it

Signal Processing on Graphs: Causal Modeling of Unstructured Data
Many applications collect a large number of time series, for example, th...
read it

Highrecall causal discovery for autocorrelated time series with latent confounders
We present a new method for linear and nonlinear, lagged and contemporan...
read it

HigherOrder Visualization of Causal Structures in Dynamics Graphs
Graph drawing and visualisation techniques are important tools for the e...
read it

A Geometric Analysis of Time Series Leading to Information Encoding and a New Entropy Measure
A time series is uniquely represented by its geometric shape, which also...
read it
Entropybased Discovery of Summary Causal Graphs in Time Series
We address in this study the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new temporal mutual information measure defined on a windowbased representation of time series. We then show how this measure relates to an entropy reduction principle that can be seen as a special case of the Probabilistic Raising Principle. We finally combine these two ingredients in a PClike algorithm to construct the summary causal graph. This algorithm is evaluated on several datasets that shows both its efficacy and efficiency.
READ FULL TEXT
Comments
There are no comments yet.