Xia, HaiyangMuskat, BirgitLi, GangPrayag, Girish2023-07-312023-07-310160-7383http://hdl.handle.net/1885/294661This research introduces a novel method for uncovering potential causal relationships in tourism literature through artificial intelligence (AI)-based counterfactual reasoning and big data. Tourism generates massive volumes of device, transaction, and user-generated data, and these can be leveraged using AI algorithms to better understand tourism-related social phenomena (Park, Xu, Jiang, Chen, & Huang, 2020). Existing tourism studies have used deductive, fuzzy, inductive, and transductive AI models (Cevikalp & Franc, 2017) to extract insights from big data, but these often fail to capture potential causal effects (Guidotti, 2022), which is problematic for two reasons. First, decision-making by tourism stakeholders cannot be improved if AI models mainly rely on spurious correlations (Law & Li, 2007). Second, the failure of capturing potential causal effects in big data diminishes its perceived value for both tourism scholars and practitioners.This research is supported by Australian Government Research Training Program (AGRTP) Scholarship.application/pdfen-AU© 2023 The Author(s). Published by Elsevier Ltd.https://creativecommons.org/licenses/by-nc-nd/4.0/Counterfactual reasoningArtificial intelligenceTourismDecision-makingBig data2023-0710.1016/j.annals.2023.103617Creative Commons Attribution-NonCommercial-NoDerivs License