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Found 6 papers, 6 papers with code
Conditional Feature Importance for Mixed Data
The CPI enables conditional FI measurement that controls for any feature dependencies by sampling valid knockoffs - hence, generating synthetic data with similar statistical properties - for the data to be analyzed.
Adversarial random forests for density estimation and generative modeling
We propose methods for density estimation and data synthesis using a novel form of unsupervised random forests.
Causal discovery under a confounder blanket
Under a structural assumption called the $\textit{confounder blanket principle}$, which we argue is essential for tractable causal discovery in high dimensions, our method accommodates graphs of low or high sparsity while maintaining polynomial time complexity.
Rational Shapley Values
Explaining the predictions of opaque machine learning algorithms is an important and challenging task, especially as complex models are increasingly used to assist in high-stakes decisions such as those arising in healthcare and finance.
Operationalizing Complex Causes: A Pragmatic View of Mediation
We examine the problem of causal response estimation for complex objects (e. g., text, images, genomics).
Testing Conditional Independence in Supervised Learning Algorithms
We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a reduced feature set.
