We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangled outcomes simultaneously. We call this approach Causally Disentangled Generation (CDG). CDG is a generative model that accurately decodes an output based on a causally disentangled representation. Our research demonstrates that adding supervised regularization to the encoder alone is insufficient for achieving a generative model with CDG, even for a simple task. Therefore, we explore the necessary and sufficient conditions for achieving CDG within a specific model. Additionally, we introduce a universal metric for evaluating the causal disentanglement of a generative model. Empirical results from both image and tabular datasets support our findings.

翻译：我们提出一种针对变分自编码器（VAE）的新型监督学习技术，使其能够同时学习因果解耦表示并生成因果解耦结果。我们将此方法称为因果解耦生成（CDG）。CDG是一种基于因果解耦表示准确解码输出的生成模型。研究表明，即使在简单任务中，仅对编码器施加监督正则化不足以实现具备CDG能力的生成模型。因此，我们探索了特定模型内实现CDG的必要充分条件。此外，我们引入了一个通用指标用于评估生成模型的因果解耦程度。来自图像和表格数据集的实验结果支持了我们的发现。