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Found 6 papers, 2 papers with code
Fundamental Properties of Causal Entropy and Information Gain
These measures, named causal entropy and causal information gain, aim to address limitations in existing information theoretical approaches for machine learning tasks where causality plays a crucial role.
Causal Entropy and Information Gain for Measuring Causal Control
Specifically, we introduce causal versions of entropy and mutual information, termed causal entropy and causal information gain, which are designed to assess how much control a feature provides over the outcome variable.
Graphical Representations for Algebraic Constraints of Linear Structural Equations Models
The observational characteristics of a linear structural equation model can be effectively described by polynomial constraints on the observed covariance matrix.
Algebraic Equivalence of Linear Structural Equation Models
Despite their popularity, many questions about the algebraic constraints imposed by linear structural equation models remain open problems.
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ.
Combining predictions from linear models when training and test inputs differ
Methods for combining predictions from different models in a supervised learning setting must somehow estimate/predict the quality of a model's predictions at unknown future inputs.
