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Before the transition of AVs to urban roads and subsequently unprecedentedchanges in traffic conditions, evaluation of transportation policies andfuturistic road design related to pedestrian crossing behavior is of vitalimportance. Recent studies analyzed the non-causal impact of various variableson pedestrian waiting time in the presence of AVs. However, we mainlyinvestigate the causal effect of traffic density on pedestrian waiting time. Wedevelop a Double/Debiased Machine Learning (DML) model in which the impact ofconfounders variable influencing both a policy and an outcome of interest isaddressed, resulting in unbiased policy evaluation. Furthermore, we try toanalyze the effect of traffic density by developing a copula-based joint modelof two main components of pedestrian crossing behavior, pedestrian stress leveland waiting time. The copula approach has been widely used in the literature,for addressing self-selection problems, which can be classified as a causalityanalysis in travel behavior modeling. The results obtained from copula approachand DML are compared based on the effect of traffic density. In DML modelstructure, the standard error term of density parameter is lower than copulaapproach and the confidence interval is considerably more reliable. Inaddition, despite the similar sign of effect, the copula approach estimates theeffect of traffic density lower than DML, due to the spurious effect ofconfounders. In short, the DML model structure can flexibly adjust the impactof confounders by using machine learning algorithms and is more reliable forplanning future policies.

Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour