Conditional Generative Models for Counterfactual Explanations
This provides a general solution for explaining model decisions across various data modalities and tasks, though it appears incremental as it builds on existing generative methods.
The authors tackled the problem of generating human-interpretable counterfactual explanations for machine learning models by proposing a flexible framework that uses conditional generative models to produce sparse, in-distribution instances matching target predictions, demonstrating effectiveness across image, time series, and tabular datasets.
Counterfactual instances offer human-interpretable insight into the local behaviour of machine learning models. We propose a general framework to generate sparse, in-distribution counterfactual model explanations which match a desired target prediction with a conditional generative model, allowing batches of counterfactual instances to be generated with a single forward pass. The method is flexible with respect to the type of generative model used as well as the task of the underlying predictive model. This allows straightforward application of the framework to different modalities such as images, time series or tabular data as well as generative model paradigms such as GANs or autoencoders and predictive tasks like classification or regression. We illustrate the effectiveness of our method on image (CelebA), time series (ECG) and mixed-type tabular (Adult Census) data.