<sc>Dagma-DCE</sc>: Interpretable, Non-Parametric Differentiable Causal Discovery

<sc>Dagma-DCE</sc>: Interpretable, Non-Parametric Differentiable Causal Discovery

We introduce <sc>Dagma-DCE</sc>, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of &#x201C;independence&#x201D; to justify the inclusion or exclusion of...

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Bibliographic Details
Main Authors: Daniel Waxman, Kurt Butler, Petar M. Djuric
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Causal discovery
differential causal effect
interpretable machine learning
Online Access:https://ieeexplore.ieee.org/document/10384714/

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