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Found 7 papers, 3 papers with code
Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation
One approach computes distances by a one-hot encoding of the categorical variables, essentially treating categorical variables as discrete-numerical, while the other expresses CMI by entropy terms where the categorical variables appear as conditions only.
Projecting infinite time series graphs to finite marginal graphs using number theory
In this work, we develop a method for projecting infinite time series graphs with repetitive edges to marginal graphical models on a finite time window.
Bootstrap aggregation and confidence measures to improve time series causal discovery
It can be combined with a range of time series causal discovery methods and provides a measure of confidence for the links of the time series graphs.
Discovering Causal Relations and Equations from Data
Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries.
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery
We apply our framework to the statistical intensity prediction of Western Pacific Tropical Cyclones (TC), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging).
Characterization of causal ancestral graphs for time series with latent confounders
In this paper, we introduce a novel class of graphical models for representing time lag specific causal relationships and independencies of multivariate time series with unobserved confounders.
High-recall causal discovery for autocorrelated time series with latent confounders
We show that existing causal discovery methods such as FCI and variants suffer from low recall in the autocorrelated time series case and identify low effect size of conditional independence tests as the main reason.
