面向因果合规解释的深度回溯反事实
Deep Backtracking Counterfactuals for Causally Compliant Explanations
AI总结：
本文提出DeepBC方法，通过Langevin Monte Carlo采样或约束优化在深度生成结构因果模型中计算回溯反事实，实现因果合规、通用且模块化的高维数据解释。
摘要：
反事实回答在改变的情况下会观察到什么的问题，因此能够提供有价值的见解。尽管反事实的经典干预性解释已被广泛研究，回溯（backtracking）则是一种研究较少的替代方案，其中所有因果律均保持不变。在本工作中，我们引入一种称为深度回溯反事实（DeepBC）的实用方法，用于在由深度生成组件构成的结构因果模型中计算回溯反事实。我们提出两种不同的方法版本——一种利用Langevin Monte Carlo采样，另一种采用约束优化——以生成高维数据的反事实。作为一种特例，我们的公式可归结为反事实解释领域中的方法。与这些方法相比，我们的方法代表了一种因果合规、通用且模块化的替代方案。我们在MNIST和CelebA的修改版本上通过实验证明了这些性质。
英文摘要：
Counterfactuals answer questions of what would have been observed under altered circumstances and can therefore offer valuable insights. Whereas the classical interventional interpretation of counterfactuals has been studied extensively, backtracking constitutes a less studied alternative where all causal laws are kept intact. In the present work, we introduce a practical method called deep backtracking counterfactuals (DeepBC) for computing backtracking counterfactuals in structural causal models that consist of deep generative components. We propose two distinct versions of our method--one utilizing Langevin Monte Carlo sampling and the other employing constrained optimization--to generate counterfactuals for high-dimensional data. As a special case, our formulation reduces to methods in the field of counterfactual explanations. Compared to these, our approach represents a causally compliant, versatile and modular alternative. We demonstrate these properties experimentally on a modified version of MNIST and CelebA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
