Core Concepts
Adversarial random forests (ARF) can be leveraged to efficiently generate plausible counterfactual explanations that are also sparse and proximal to the original instance.
Abstract
The paper proposes two algorithms that utilize ARF to generate counterfactual explanations:
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MOCARF: Integrates ARF into the multi-objective counterfactual explanation (MOC) framework to speed up the counterfactual search and find more plausible counterfactuals.
- ARF is used to estimate the plausibility of counterfactuals, replacing the original plausibility measure in MOC.
- FORGE, the generative component of ARF, is used to sample plausible candidates in the mutation step of the NSGA-II optimization.
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ARF-based Generator:
- Directly uses ARF to efficiently generate many relevant counterfactuals.
- Leverages FORGE to generate plausible data points.
- Selects the features to change based on their local feature importance, ensuring sparsity.
- Only returns the valid and Pareto-optimal set of counterfactuals.
The key advantages of the proposed methods are:
- Improved plausibility of generated counterfactuals compared to existing approaches without major sacrifices in sparsity, proximity, and runtime.
- ARF handles mixed tabular data directly, improving data-efficiency.
- The methods are computationally efficient and require minimal tuning.
The paper evaluates the proposed methods on synthetic datasets and demonstrates their superiority over competing approaches. An illustrative real-world application on coffee quality prediction is also provided.
Stats
The data-generating process constructed three illustrative two-dimensional datasets (cassini, two sines, three blobs) and four datasets from randomly sampled Bayesian networks of dimensionality 5, 10, and 20 (bn_5, bn_10, bn_20).
An XGBoost model was fitted on sampled datasets Dtrain of size 5,000.