Causal Discovery from Conditionally Stationary Time Series
arxiv(2021)
摘要
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.
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关键词
causal discovery,temporal data,graph neural network,time series,non-stationary,probabilistic modelling
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