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Found 6 papers, 3 papers with code
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
This particular test-wise deletion procedure, in which we perform CI tests on the samples without zeros for the conditioned variables, can be seamlessly integrated with existing structure learning approaches including constraint-based and greedy score-based methods, thus giving rise to a principled framework for GRNI in the presence of dropouts.
Local Causal Discovery with Linear non-Gaussian Cyclic Models
Local causal discovery is of great practical significance, as there are often situations where the discovery of the global causal structure is unnecessary, and the interest lies solely on a single target variable.
On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors
Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution - nonstationarity, and causal factors that are important but unknown/unobserved.
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models
Causal discovery under measurement error aims to recover the causal graph among unobserved target variables from observations made with measurement error.
ML4C: Seeing Causality Through Latent Vicinity
Starting from seeing that SCL is not better than random guessing if the learning target is non-identifiable a priori, we propose a two-phase paradigm for SCL by explicitly considering structure identifiability.
What do CNN neurons learn: Visualization & Clustering
In recent years convolutional neural networks (CNN) have shown striking progress in various tasks.
