论文标题
部分可观测时空混沌系统的无模型预测
Weakly supervised causal representation learning
论文作者
论文摘要
仅凭观察数据就不可能学习高级因果表示以及来自非结构化低级数据（例如像素）的因果模型。我们在温和的假设下证明，在弱监督的环境中可以识别该表示形式。这涉及一个随机，未知干预措施之前和之后带有配对样品的数据集，但没有其他标签。然后，我们引入隐式潜在因果模型，代表因果变量和因果结构的变异自动编码器，而无需优化显式离散图结构。在简单的图像数据（包括模拟机器人操作的新数据集）上，我们证明了这样的模型可以可靠地识别因果结构和分离因果变量。
Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under mild assumptions that this representation is however identifiable in a weakly supervised setting. This involves a dataset with paired samples before and after random, unknown interventions, but no further labels. We then introduce implicit latent causal models, variational autoencoders that represent causal variables and causal structure without having to optimize an explicit discrete graph structure. On simple image data, including a novel dataset of simulated robotic manipulation, we demonstrate that such models can reliably identify the causal structure and disentangle causal variables.
