Tourism significantly affects the economies of many countries. Understanding the causal relationship between the length of overnight stay and traveller's expenditure is crucial for stakeholders to characterize spending profiles and to design marketing strategies. Causal mechanisms differ between personal and work-related travel because the decision-making processes have different drivers and constraints. We apply context-specific independence relations to model causal mechanisms in contexts specified by trip purpose and identify the causal effect of the length of stay on expenditure. Using the international visitor survey data on foreign travellers to Finland, we fit a hierarchical Bayesian model to estimate the posterior distribution of the counterfactual expenditure due to extending the length of stay by one night. We also perform a Bayesian sensitivity analysis of the estimated causal effect with respect to omitted variable bias.

翻译：旅游业对许多国家的经济具有显著影响。理解过夜停留时长与旅行者消费之间的因果关系，对于利益相关者刻画消费特征和设计营销策略至关重要。由于决策过程的驱动因素和约束条件不同，个人旅行与工作相关旅行的因果机制存在差异。我们应用情境特定独立性关系，对由旅行目的所定义情境中的因果机制进行建模，并识别停留时长对消费的因果效应。利用针对赴芬兰外国旅行者的国际游客调查数据，我们拟合了一个分层贝叶斯模型，以估计因延长一晚停留时间而产生的反事实消费的后验分布。此外，我们还针对估计的因果效应进行了关于遗漏变量偏误的贝叶斯敏感性分析。