Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has shown that one can reconstruct the causal variables from temporal sequences of observations under the assumption that there are no instantaneous causal relations between them. In practical applications, however, our measurement or frame rate might be slower than many of the causal effects. This effectively creates "instantaneous" effects and invalidates previous identifiability results. To address this issue, we propose iCITRIS, a causal representation learning method that allows for instantaneous effects in intervened temporal sequences when intervention targets can be observed, e.g., as actions of an agent. iCITRIS identifies the potentially multidimensional causal variables from temporal observations, while simultaneously using a differentiable causal discovery method to learn their causal graph. In experiments on three datasets of interactive systems, iCITRIS accurately identifies the causal variables and their causal graph.

翻译：因果表征学习是从高维观测数据（如图像）中识别潜在因果变量及其关系的任务。近期研究表明，在假设变量之间不存在瞬时因果关系的条件下，可以从时间序列观测中重建因果变量。然而在实际应用中，测量或帧率可能慢于许多因果效应的发生速度，这实质上产生了"瞬时"效应，使得先前的可辨识性结论失效。为解决该问题，我们提出iCITRIS方法——一种允许干预时间序列中存在瞬时效应的因果表征学习算法，其前提是可观测干预目标（如智能体的动作）。iCITRIS能从时间观测中识别潜在的多维因果变量，同时采用可微分的因果发现方法学习其因果图。在三个交互系统数据集的实验中，iCITRIS准确识别了因果变量及其因果图。