Dynamic Causal Structure Discovery and Causal Effect Estimation

TL;DR

This paper introduces a novel framework for discovering and estimating dynamic causal structures that change over time, using a basis approximation and autoregressive models, demonstrated through simulations and COVID-19 data analysis.

Contribution

It develops a new method for modeling time-varying causal graphs with an autoregressive structure, extending existing causal discovery approaches to dynamic settings.

Findings

Effectiveness demonstrated through simulations.

Applied to COVID-19 data to analyze policy impact.

Provides both causal estimates and future predictions.

Abstract

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.