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Found 3 papers, 2 papers with code
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
We demonstrate a somewhat surprising connection between this problem and causal discovery in the presence of unobserved parentless causes, in the sense that there is a mapping, given by the mixing matrix, between the underlying models to be inferred in these problems.
Causal Discovery in Linear Structural Causal Models with Deterministic Relations
Linear structural causal models (SCMs)-- in which each observed variable is generated by a subset of the other observed variables as well as a subset of the exogenous sources-- are pervasive in causal inference and casual discovery.
Information Theoretic Measures for Fairness-aware Feature Selection
In particular, we first propose information theoretic measures which quantify the impact of different subsets of features on the accuracy and discrimination of the decision outcomes.
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