Counterfactual Explanations via Latent Space Projection and Interpolation

TL;DR

This paper introduces SharpShooter, a fast and effective method for generating plausible counterfactual explanations in classification tasks by leveraging latent space interpolation, suitable for real-time applications.

Contribution

SharpShooter is a novel approach that efficiently generates realistic counterfactuals through latent space interpolation, outperforming existing methods in speed and quality.

Findings

Competitive quality on tabular and image datasets

Significantly faster than comparable methods

Excels in realism and suitability for high-velocity applications

Abstract

Counterfactual explanations represent the minimal change to a data sample that alters its predicted classification, typically from an unfavorable initial class to a desired target class. Counterfactuals help answer questions such as "what needs to change for this application to get accepted for a loan?". A number of recently proposed approaches to counterfactual generation give varying definitions of "plausible" counterfactuals and methods to generate them. However, many of these methods are computationally intensive and provide unconvincing explanations. Here we introduce SharpShooter, a method for binary classification that starts by creating a projected version of the input that classifies as the target class. Counterfactual candidates are then generated in latent space on the interpolation line between the input and its projection. We then demonstrate that our framework translates core characteristics of a sample to its counterfactual through the use of learned representations. Furthermore, we show that SharpShooter is competitive across common quality metrics on tabular and image datasets while being orders of magnitude faster than two comparable methods and excels at measures of realism, making it well-suited for high velocity machine learning applications which require timely explanations.