Counterfactuals can offer valuable insights by answering what would have been
observed under altered circumstances, conditional on a factual observation.
Whereas the classical interventional interpretation of counterfactuals has been
studied extensively, backtracking constitutes a less studied alternative the
backtracking principle has emerged as an alternative philosophy where all
causal laws are kept intact. In the present work, we introduce a practical
method for computing backtracking counterfactuals in structural causal models
that consist of deep generative components. To this end, we impose conditions
on the structural assignments that enable the generation of counterfactuals by
solving a tractable constrained optimization problem in the structured latent
space of a causal model. Our formulation also facilitates a comparison with
methods in the field of counterfactual explanations. Compared to these, our
method represents a versatile, modular and causally compliant alternative. We
demonstrate these properties experimentally on a modified version of MNIST and
CelebA.

在结构因果模型中，我们引入了一种计算回溯反事实的实用方法，该方法对深度生成组件进行条件设定以解决在可控制因果模型的结构潜空间中可生成反事实的问题。相比其他方法，我们的方法具有多功能、模块化和满足因果关系的优势。通过在 MNIST 和 CelebA 的修改版本上实验，我们证明了这些特性。