Causal discovery, i.e., inferring underlying causal relationships from observational data, has been shown to be highly challenging for AI systems. In time series modeling context, traditional causal discovery methods mainly consider constrained scenarios with fully observed variables and/or data from stationary time-series. We develop a causal discovery approach to handle a wide class of non-stationary time-series that are conditionally stationary, where the non-stationary behaviour is modeled as stationarity conditioned on a set of (possibly hidden) state variables. Named State-Dependent Causal Inference (SDCI), our approach is able to recover the underlying causal dependencies, provably with fully-observed states and empirically with hidden states. The latter is confirmed by experiments on synthetic linear system and nonlinear particle interaction data, where SDCI achieves superior performance over baseline causal discovery methods. Improved results over non-causal RNNs on modeling NBA player movements demonstrate the potential of our method and motivate the use of causality-driven methods for forecasting.

翻译：因果发现，即从观测数据中推断潜在的因果关系，已被证明对人工智能系统极具挑战性。在时间序列建模背景下，传统的因果关系发现方法主要考虑具有完全观测变量和/或来自平稳时间序列数据的受限场景。我们开发了一种因果发现方法，用于处理广泛的条件平稳非平稳时间序列，其中非平稳行为被建模为以一组（可能隐藏的）状态变量为条件的平稳性。我们的方法名为状态依赖因果推断（State-Dependent Causal Inference, SDCI），能够恢复潜在的因果依赖关系，在状态完全可观测的情况下可证明其有效性，而在状态隐藏的情况下则通过实验验证。后者通过合成线性系统和非线性粒子相互作用数据的实验得到确认，其中SDCI在基线因果发现方法上取得了优越性能。在模拟NBA球员运动数据上，相较于非因果RNN的改进结果展示了我们方法的潜力，并激发了使用因果驱动方法进行预测的动机。