Gradient-based Counterfactual Explanations using Tractable Probabilistic Models

TL;DR

This paper introduces a fast, gradient-based method for generating realistic counterfactual explanations in machine learning using tractable probabilistic models, avoiding complex optimization.

Contribution

It presents a novel approach that uses only two gradient computations to produce counterfactuals, improving speed and realism over existing methods.

Findings

Method is faster than traditional optimization-based approaches.

Produces more realistic counterfactual examples.

Empirical results show clear advantages over prior methods.

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.