Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic:
  A Doubly Robust Causal Machine Learning Approach
Shuang Li Ziyuan Pu Zhiyong Cui Seunghyeon Lee Xiucheng Guo Dong Ngoduy
Highway traffic crashes exert a considerable impact on both transportation
systems and the economy. In this context, accurate and dependable emergency
responses are crucial for effective traffic management. However, the influence
of crashes on traffic status varies across diverse factors and may be biased
due to selection bias. Therefore, there arises a necessity to accurately
estimate the heterogeneous causal effects of crashes, thereby providing
essential insights to facilitate individual-level emergency decision-making.
This paper proposes a novel causal machine learning framework to estimate the
causal effect of different types of crashes on highway speed. The Neyman-Rubin
Causal Model (RCM) is employed to formulate this problem from a causal
perspective. The Conditional Shapley Value Index (CSVI) is proposed based on
causal graph theory to filter adverse variables, and the Structural Causal
Model (SCM) is then adopted to define the statistical estimand for causal
effects. The treatment effects are estimated by Doubly Robust Learning (DRL)
methods, which combine doubly robust causal inference with classification and
regression machine learning models. Experimental results from 4815 crashes on
Highway Interstate 5 in Washington State reveal the heterogeneous treatment
effects of crashes at varying distances and durations. The rear-end crashes
cause more severe congestion and longer durations than other types of crashes,
and the sideswipe crashes have the longest delayed impact. Additionally, the
findings show that rear-end crashes affect traffic greater at night, while
crash to objects has the most significant influence during peak hours.
Statistical hypothesis tests, error metrics based on matched "counterfactual
outcomes", and sensitive analyses are employed for assessment, and the results
validate the accuracy and effectiveness of our method.