Navigating the Structured What-If Spaces: Counterfactual Generation via
  Structured Diffusion
Nishtha Madaan Srikanta Bedathur
Generating counterfactual explanations is one of the most effective
approaches for uncovering the inner workings of black-box neural network models
and building user trust. While remarkable strides have been made in generative
modeling using diffusion models in domains like vision, their utility in
generating counterfactual explanations in structured modalities remains
unexplored. In this paper, we introduce Structured Counterfactual Diffuser or
SCD, the first plug-and-play framework leveraging diffusion for generating
counterfactual explanations in structured data. SCD learns the underlying data
distribution via a diffusion model which is then guided at test time to
generate counterfactuals for any arbitrary black-box model, input, and desired
prediction. Our experiments show that our counterfactuals not only exhibit high
plausibility compared to the existing state-of-the-art but also show
significantly better proximity and diversity.