Measuring axiomatic soundness of counterfactual image models

Understand

We present a general framework for evaluating image counterfactuals.

• The power and flexibility of deep generative models make them valuable tools for learning mechanisms in structural causal models.
• However, their flexibility makes counterfactual identifiability impossible in the general case.
• Motivated by these issues, we revisit Pearl's axiomatic definition of counterfactuals to determine the necessary constraints of any counterfactual inference model: composition, reversibility, and effectiveness.