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Found 4 papers, 3 papers with code
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome.
arfpy: A python package for density estimation and generative modeling with adversarial random forests
This paper introduces $\textit{arfpy}$, a python implementation of Adversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweight procedure for synthesizing new data that resembles some given data.
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.
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