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Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations
A new perspective has been provided based on the principle of Independence of Causal Mechanisms (ICM), leading to the Spectral Independence Criterion (SIC), postulating that the power spectral density (PSD) of the cause time series is uncorrelated with the squared modulus of the frequency response of the filter generating the effect.
Learning from Positive and Unlabeled Data by Identifying the Annotation Process
Yet the annotation process is an important part of the data collection, and in many cases it naturally depends on certain features of the data (e. g., the intensity of an image and the size of the object to be detected in the image).
Group invariance principles for causal generative models
The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms.
Telling cause from effect in deterministic linear dynamical systems
Assuming the effect is generated by the cause trough a linear system, we propose a new approach based on the hypothesis that nature chooses the "cause" and the "mechanism that generates the effect from the cause" independent of each other.
Justifying Information-Geometric Causal Inference
Information Geometric Causal Inference (IGCI) is a new approach to distinguish between cause and effect for two variables.
