Dynamic Causal Structure Discovery and Causal Effect Estimation
This work addresses the limitation of assuming constant causal relationships over time in causal discovery, which is crucial for dynamic real-world applications like epidemiology and social sciences, though it is incremental as it builds on existing score-based approaches.
The paper tackles the problem of static causal relationships in existing causal structure learning methods by developing a framework for modeling dynamic, time-varying causal graphs, and demonstrates its application in COVID-19 policy analysis to show how policy restriction effects change over time.
To represent the causal relationships between variables, a directed acyclic graph (DAG) is widely utilized in many areas, such as social sciences, epidemics, and genetics. Many causal structure learning approaches are developed to learn the hidden causal structure utilizing deep-learning approaches. However, these approaches have a hidden assumption that the causal relationship remains unchanged over time, which may not hold in real life. In this paper, we develop a new framework to model the dynamic causal graph where the causal relations are allowed to be time-varying. We incorporate the basis approximation method into the score-based causal discovery approach to capture the dynamic pattern of the causal graphs. Utilizing the autoregressive model structure, we could capture both contemporaneous and time-lagged causal relationships while allowing them to vary with time. We propose an algorithm that could provide both past-time estimates and future-time predictions on the causal graphs, and conduct simulations to demonstrate the usefulness of the proposed method. We also apply the proposed method for the covid-data analysis, and provide causal estimates on how policy restriction's effect changes.