TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data

TL;DR

TS-CausalNN is a deep learning model designed to uncover both contemporaneous and lagged causal relationships in complex, non-linear, and non-stationary time series data, outperforming existing methods.

Contribution

The paper introduces TS-CausalNN, a novel neural network architecture that handles non-stationarity and non-linearity for causal discovery in time series data.

Findings

Outperforms state-of-the-art causal discovery methods on synthetic datasets.

Accurately infers causal graphs aligned with domain knowledge in real-world data.

Handles non-stationarity and non-linearity effectively.

Abstract

The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data. However, the majority of current time series causal discovery methods assume stationarity and linear relations in data, making them infeasible for the task. Further, the recent deep learning-based methods rely on the traditional causal structure learning approaches making them computationally expensive. In this paper, we propose a Time-Series Causal Neural Network (TS-CausalNN) - a deep learning technique to discover contemporaneous and lagged causal relations simultaneously. Our proposed architecture comprises (i) convolutional blocks comprising parallel custom causal layers, (ii) acyclicity constraint, and (iii) optimization techniques using the augmented Lagrangian approach. In addition to the simple parallel design, an advantage of the proposed model is that it naturally handles the non-stationarity and non-linearity of the data. Through experiments on multiple synthetic and real world datasets, we demonstrate the empirical proficiency of our proposed approach as compared to several state-of-the-art methods. The inferred graphs for the real world dataset are in good agreement with the domain understanding.