Gradient-based Counterfactual Explanations using Tractable Probabilistic Models

• URL: http://arxiv.org/abs/2205.07774v1
• Date: Mon, 16 May 2022 16:05:43 GMT
• Title: Gradient-based Counterfactual Explanations using Tractable Probabilistic Models
• Authors: Xiaoting Shao, Kristian Kersting
• Abstract summary: Counterfactual examples are an appealing class of explanations for machine learning models. Current approaches primarily solve this task by a complex optimization. We propose a novel approach to deal with these problems using only two gradient computations.
• Score: 20.770168600755877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Counterfactual examples are an appealing class of post-hoc explanations for machine learning models. Given input $x$ of class $y_1$, its counterfactual is a contrastive example $x^\prime$ of another class $y_0$. Current approaches primarily solve this task by a complex optimization: define an objective function based on the loss of the counterfactual outcome $y_0$ with hard or soft constraints, then optimize this function as a black-box. This "deep learning" approach, however, is rather slow, sometimes tricky, and may result in unrealistic counterfactual examples. In this work, we propose a novel approach to deal with these problems using only two gradient computations based on tractable probabilistic models. First, we compute an unconstrained counterfactual $u$ of $x$ to induce the counterfactual outcome $y_0$. Then, we adapt $u$ to higher density regions, resulting in $x^{\prime}$. Empirical evidence demonstrates the dominant advantages of our approach.

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