Counterfactual Explanations for Arbitrary Regression Models

• URL: http://arxiv.org/abs/2106.15212v1
• Date: Tue, 29 Jun 2021 09:53:53 GMT
• Title: Counterfactual Explanations for Arbitrary Regression Models
• Authors: Thomas Spooner, Danial Dervovic, Jason Long, Jon Shepard, Jiahao Chen, Daniele Magazzeni
• Abstract summary: We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation. Our method is a globally convergent search algorithm with support for arbitrary regression models and constraints like feature sparsity and actionable recourse.
• Score: 8.633492031855655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally convergent search algorithm with support for arbitrary regression models and constraints like feature sparsity and actionable recourse, and furthermore can answer multiple counterfactual questions in parallel while learning from previous queries. We formulate CFE search for regression models in a rigorous mathematical framework using differentiable potentials, which resolves robustness issues in threshold-based objectives. We prove that in this framework, (a) verifying the existence of counterfactuals is NP-complete; and (b) that finding instances using such potentials is CLS-complete. We describe a unified algorithm for CFEs using a specialised acquisition function that composes both expected improvement and an exponential-polynomial (EP) family with desirable properties. Our evaluation on real-world benchmark domains demonstrate high sample-efficiency and precision.

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