What Really Drives Behavior Change? A Causal Analysis of Impaired Driving Prevention Factors Using Machine Learning and Survey Data

Impaired driving kills over 10,000 people in the US each year. Whether educational campaigns cause behavior change remains unclear because many evaluations rely on correlational rather than causal evidence. This study addresses a fundamental question in transportation safety: Do educational campaigns truly cause changes in impaired driving behavior? We applied advanced causal inference methods, Linear Non-Gaussian Acyclic Model (LiNGAM), and the DoWhy framework, to survey data from 876 respondents to identify genuine causal drivers of behavior change intentions. Our approach combined LiNGAM-based causal discovery to learn the complete directed acyclic graph among variables, followed by DoWhy’s systematic causal effect estimation with backdoor adjustment to estimate average treatment effects (ATE). Multiple refutation tests validated the robustness of our findings. The results showed that perceived campaign effectiveness (ATE = 0.264), concern about neighborhood driving under the influence incidents (0.254), and belief in sobriety measure effectiveness (0.195) emerged as significant positive causal drivers of behavior change likelihood. In contrast, traditional awareness-based interventions, including campaign exposure (−0.125) and enforcement knowledge (−0.101), demonstrated negative causal effects, challenging conventional assumptions about information-based prevention strategies. These findings suggest that successful impaired driving prevention requires shifting from information dissemination to strategies that actively cultivate risk perceptions and intervention efficacy beliefs. This study’s findings show how modern causal discovery and validation tools can provide actionable evidence for policymakers, demonstrating that campaigns perceived as credible and locally relevant drive meaningful behavioral shifts, while mere exposure to educational content may be counterproductive.