Wei Chen

Recovering causal structures from non-stationary time series data is a challenging task across numerous domains. Conventional methods often assume that the non-stationarity of data is indexed by a specific time, domain, or regime, rendering them inadequate for complex scenarios driven by unobserved external factors. To address these limitations, we adopt a latent-variable perspective, viewing non-stationarity as arising from unobserved external factors whose changing states induce temporal variations in causal mechanisms. This perspective motivates a unified framework that exploits global causal structure to guide local causal discovery, rather than treating temporal segmentation and causal structure learning as independent tasks. Specifically, we propose a two-stage algorithm. First, we leverage LPCMCI as a global structural heuristic to identify observed variables potentially affected by latent confounding. Second, we monitor the statistical stability of the identified variables to partition the time series into approximately stationary intervals, within which PCMCI+ is applied to recover local causal structures. By integrating global structural analysis with stability-guided segmentation, our framework narrows the search space and facilitates the recovery of temporal variations in causal relationships, including changes in causal strengths and graph structure. Experiments on synthetic benchmarks and real-world datasets demonstrate the effectiveness of the proposed approach in recovering non-stationary causal structures.

LEARNING POINTS The structural causal model (SCM) is a next‐generation artificial intelligence method. It can transparently reveal the causal graph of a system, make predictions based on causal mechanisms, enable individualised inference and facilitate virtual intervention experiments. Each SCM method has its own advantages and limitations that should be considered in practice. As the field develops rapidly, further breakthroughs in both SCM methods and precision medicine are anticipated. Currently, there are only a few applications of SCM in psychiatry research. We encourage the use of it as a tool for advancing precision medicine in psychiatry.

Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interferences are the additional effects of external factors on observed variables. Since these external factors are often unknown, we introduce latent variables to represent these unobserved factors that affect the observed data. Specifically, to capture the causal strength and adjacency information, we propose a new temporal latent variable structural causal model, incorporating causal strength and adjacency coefficients that represent the causal relationships between variables. Considering that expert knowledge can provide information about unknown interferences in certain scenarios, we develop a method that facilitates the incorporation of prior knowledge into parameter learning based on Variational Inference, to guide the model estimation. Experimental results demonstrate the stability and accuracy of our proposed method.

Causal discovery plays a pivotal role in scientific inquiry and subsequent applications in prediction or decision-making. While many methods have been proposed, many of them rely on independence tests. However, these tests are difficult to implement and computationally intensive. In this article, we aim to propose a direct and computationally efficient method to determine the causal relationship between two observed variables in the linear non-Gaussian case. Building on the insight that cumulants provide information about the shape of a probability distribution, we show that interestingly, the (in)dependence between two observed variables can be directly inferred from the difference in the product of certain joint cumulants of these variables. This concept is named the cause difference criterion. Based on this criterion, we introduce two practical methods, high-order cumulant (HC) and HC-linear non-Gaussian acyclic model (LiNGAM), for causal discovery in the high-dimensional case. Theoretical analyses ensure the identifiability of the proposed criteria and methods. Experimental results indicate that our methods outperform most existing methods.