论文标题
学习潜在的因果动态
Learning Latent Causal Dynamics
论文作者
论文摘要
时间序列建模的一个关键挑战是如何学习和快速纠正未知分布变化下的模型。在这项工作中，我们提出了一个名为Lily的原则性框架，以首先恢复时间延迟的潜在因果变量，并在不同的分布变化下从测量的时间数据中确定其关系。然后将校正步骤提出，因为从新环境中学习了一些样本，以学习低维变化因子，从而利用了已确定的因果结构。具体而言，该框架将未知的分布转移到由固定动力学和时间变化的潜在因果关系以及观察过程中的全局变化引起的过渡分布变化。我们在固定动力学和变化下从其非线性混合物中建立了非参数潜在因果动力学的可识别性理论。通过实验，我们表明，从不同分布变化下观察到的变量可靠地鉴定出时间延迟的潜在因果影响。通过利用这种变化的模块化表示，我们可以有效地学习仅使用少数样本在未知分布变化下纠正模型。
One critical challenge of time-series modeling is how to learn and quickly correct the model under unknown distribution shifts. In this work, we propose a principled framework, called LiLY, to first recover time-delayed latent causal variables and identify their relations from measured temporal data under different distribution shifts. The correction step is then formulated as learning the low-dimensional change factors with a few samples from the new environment, leveraging the identified causal structure. Specifically, the framework factorizes unknown distribution shifts into transition distribution changes caused by fixed dynamics and time-varying latent causal relations, and by global changes in observation. We establish the identifiability theories of nonparametric latent causal dynamics from their nonlinear mixtures under fixed dynamics and under changes. Through experiments, we show that time-delayed latent causal influences are reliably identified from observed variables under different distribution changes. By exploiting this modular representation of changes, we can efficiently learn to correct the model under unknown distribution shifts with only a few samples.
