Causal Discovery and Inference towards Urban Elements and Associated Factors
This work addresses the challenge of accurate causal inference in urban computing for researchers and planners, but it is incremental as it builds on existing causal discovery methods applied to a specific domain.
The paper tackles the problem of uncovering causal relationships among urban elements like citizens, locations, and mobility, which are often obscured by confounding effects in correlation analysis, and proposes a reinforcement learning-based framework that discovers a hierarchical causal graph and improves urban mobility prediction performance.
To uncover the city's fundamental functioning mechanisms, it is important to acquire a deep understanding of complicated relationships among citizens, location, and mobility behaviors. Previous research studies have applied direct correlation analysis to investigate such relationships. Nevertheless, due to the ubiquitous confounding effects, empirical correlation analysis may not accurately reflect underlying causal relationships among basic urban elements. In this paper, we propose a novel urban causal computing framework to comprehensively explore causalities and confounding effects among a variety of factors across different types of urban elements. In particular, we design a reinforcement learning algorithm to discover the potential causal graph, which depicts the causal relations between urban factors. The causal graph further serves as the guidance for estimating causal effects between pair-wise urban factors by propensity score matching. After removing the confounding effects from correlations, we leverage significance levels of causal effects in downstream urban mobility prediction tasks. Experimental studies on open-source urban datasets show that the discovered causal graph demonstrates a hierarchical structure, where citizens affect locations, and they both cause changes in urban mobility behaviors. Experimental results in urban mobility prediction tasks further show that the proposed method can effectively reduce confounding effects and enhance performance of urban computing tasks.