Conventional modelling related to travel behaviours is facing challenges from rapidly changing demand dynamics, especially in a post-pandemic context with more volatile and elastic demand. In response to this challenge and to capitalize on the potential of ubiquitous travel trajectory data (such as smart card or mobile phone data), this study proposes data-driven causal behaviour modelling. The approach includes causal discovery and causal inference methods. For causal discovery, we use classic algorithms, like PC and GES, to learn a directed acyclic graph representing the causal relationships among the variables. Using this causal graph, we define a structural equation model and estimate its corresponding parameters, allowing causal inference of the (conditional) average treatment effect. The approach is validated with expert knowledge using a case study for behaviour response of passengers to a pre-peak fare discount incentive in the mass transit systems in Hong Kong. It also presents a comparative analysis of the causal inference results with those from the traditional transport model approach, e.g., logistic regression. We demonstrate the use of causal inference on a learned causal structure, which allows identification of the important factors, estimation of their causal effects, and quantification of the unique contribution of the intervention policy. Importantly, we will illustrate how to derive policy insights not only for future scenarios (what if it is…), but also from counterfactual analysis of historical actions (what if it had been…). Our approach advances data-driven modelling of casual human behaviour dynamics to support policy developments and managerial interventions.