论文标题
深层的结构性因果模型，用于可进行的反事实推断
Deep Structural Causal Models for Tractable Counterfactual Inference
论文作者
论文摘要
我们为建立具有深度学习组成部分的结构因果模型（SCM）的一般框架。所提出的方法采用标准化的流和变异推理，以实现外源性噪声变量的可触及推断 - 现有深层因果学习方法所缺少的反事实推断的关键步骤。我们的框架已在基于MNIST的合成数据集以及脑MRI扫描的现实医学数据集上进行了验证。我们的实验结果表明，我们可以成功训练能够掌握所有三个层次的珍珠因果关系阶梯的深层SCM：关联，干预和反事实，从而为在成像应用程序及其他地区中回答因果问题的有力新方法提供了强大的新方法。我们所有实验的代码均可在https://github.com/biomedia-mira/deepscm上获得。
We formulate a general framework for building structural causal models (SCMs) with deep learning components. The proposed approach employs normalising flows and variational inference to enable tractable inference of exogenous noise variables - a crucial step for counterfactual inference that is missing from existing deep causal learning methods. Our framework is validated on a synthetic dataset built on MNIST as well as on a real-world medical dataset of brain MRI scans. Our experimental results indicate that we can successfully train deep SCMs that are capable of all three levels of Pearl's ladder of causation: association, intervention, and counterfactuals, giving rise to a powerful new approach for answering causal questions in imaging applications and beyond. The code for all our experiments is available at https://github.com/biomedia-mira/deepscm.
