Visible or Covert? The Causal Effect of Inspector Visibility on Fare Evasion Detection: A Causal Machine Learning and Policy Learning Approach

Fare evasion generates substantial revenue losses for public transport operators and is typically combated through fare inspections, yet little is known about how the mode of inspection-uniformed versus plainclothes-affects detection efficiency. Using a unique dataset of 21,727 inspection records from PostAuto, the largest regional bus operator in Switzerland, we apply causal machine learning to estimate the causal effect of inspector visibility on inspection efficiency, defined as detected fare evaders per inspection hour. Our results indicate that plainclothes inspections are, on average, significantly more effective than uniformed inspections, with an estimated average treatment effect of -0.173 incidents per hour, corresponding to a relative reduction of approximately 26%. Heterogeneity analyses find no evidence of systematic effect variation across contextual characteristics, suggesting that the superiority of plainclothes inspections is robust and pervasive across the PostAuto network. When applying optimal policy learning (based on policy trees) to optimally target subgroups by one or the other treatment depending on relative effectiveness, plainclothes inspections are recommended for the large majority of contexts (83.3%), with uniformed inspections suggested only for lines characterised by a below-median share of foreign residents and above-median population size.