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Found 4 papers, 1 papers with code
Probabilistic Easy Variational Causal Effect
In this paper, on the one hand, for the case that $X$ and $Z$ are continuous, by using the ideas from the total variation and the flux of $g$, we develop a point of view in causal inference capable of dealing with a broad domain of causal problems.
Probabilistic Variational Causal Effect as A new Theory for Causal Reasoning
Indeed, we provide a direct causal effect formula called Probabilistic vAriational Causal Effect (PACE) and its variations satisfying some ideas and postulates.
A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal Reasoning
In this paper, we introduce a fundamental framework to create a bridge between Probability Theory and Fuzzy Logic.
On the geometric and Riemannian structure of the spaces of group equivariant non-expansive operators
Group equivariant non-expansive operators have been recently proposed as basic components in topological data analysis and deep learning.
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