Neural Time-Invariant Causal Discovery from Time Series Data

Causal structure learning from observational data is an active field of research over the past decades. Although many approaches exist, such as constrained-based methods and score-based methods including the emerging deep learning-based methods, most of them address the static, non-dynamic setting. In this paper, we propose a score-based causal discovery algorithm named Neural Time-invariant Causal Discovery (NTiCD), which learns summary causal graphs from multivariate time series data based on the principle of Granger causality. NTiCD is a continuous optimization-based technique that leverages the power of deep neural networks to compute the score values. To this end, we use an LSTM to obtain the hidden non-linear representations of temporal variables in the time series data. Then, these features are aggregated using graph convolutional networks and decoded using an MLP that outputs the forecast of the future data values in the time series. The model is optimized based on a score function subject to regularized loss. The final output is a summary causal graph that captures the time-invariant causal relations within and between time series. We evaluate the performance of our algorithm on several synthetic and real datasets. The result analysis over a number of different datasets demonstrates the improvement in the accuracy of causal structure discovery of temporal data compared to other state-of-the-art methods.