Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour
利用去偏机器学习估计城市交通对行人过街行为的因果效应AI 解读 ↓
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利用去偏机器学习估计城市交通对行人过街行为的因果效应
- 研究问题
- 在自动驾驶车辆进入城市交通的背景下，交通密度如何因果性地影响行人等待时间，以及混杂和自选择如何影响效应估计？
- 方法与思路
- 作者使用双重／去偏机器学习调整同时影响交通密度与等待时间的混杂因素，并与联合建模行人压力和等待时间的 copula 方法比较。
- 材料陈述的贡献
- 摘要称，两种方法估计的效应方向一致，但双重机器学习估计更大、标准误更小；作者据此认为其政策评估更可靠。
- 阅读关注点
- 应核对交通密度的识别条件、混杂变量选择与交叉拟合过程，以及两种方法是否估计相同对象、置信区间可靠性如何验证。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核验；摘要未说明识别假设，较小标准误本身不能证明无偏性或置信区间覆盖更准确。
原文摘要
Before the transition of AVs to urban roads and subsequently unprecedented changes in traffic conditions, evaluation of transportation policies and futuristic road design related to pedestrian crossing behavior is of vital importance. Recent studies analyzed the non-causal impact of various variables on pedestrian waiting time in the presence of AVs. However, we mainly investigate the causal effect of traffic density on pedestrian waiting time. We develop a Double/Debiased Machine Learning (DML) model in which the impact of confounders variable influencing both a policy and an outcome of interest is addressed, resulting in unbiased policy evaluation. Furthermore, we try to analyze the effect of traffic density by developing a copula-based joint model of two main components of pedestrian crossing behavior, pedestrian stress level and waiting time. The copula approach has been widely used in the literature, for addressing self-selection problems, which can be classified as a causality analysis in travel behavior modeling. The results obtained from copula approach and DML are compared based on the effect of traffic density. In DML model structure, the standard error term of density parameter is lower than copula approach and the confidence interval is considerably more reliable. In addition, despite the similar sign of effect, the copula approach estimates the effect of traffic density lower than DML, due to the spurious effect of confounders. In short, the DML model structure can flexibly adjust the impact of confounders by using machine learning algorithms and is more reliable for planning future policies.