Heterogeneous and differential treatment effect analysis of safety improvements on freeways using causal inference

Evaluating safety effectiveness of freeway design improvements is crucial for enhancing overall safety and confirming the efficacy of specific measures implemented. Limited research has addressed treatment heterogeneities that influence crash outcomes, and previous studies have often been susceptible to confounding biases, which may distort causal inference results. To mitigate confounding biases and establish reliable causal relationships between crashes and treatment interventions, this study employed a causal forest (CF) model to assess the safety efficacy of freeway exit improvements - including lane control, traffic signs, speed-limit signs, and crash attenuators - on freeways in Suzhou, China. We compared naïve and empirical Bayes before-after methods against the Average Treatment Effect (ATE) estimated by the CF approach. Geometric design and traffic operation characteristics were then considered in measuring the Heterogeneous Treatment Effects (HTE) of these improvements, with the aim of identifying road features where treatment benefits were most pronounced. Additionally, a Differential Treatment Effects (DTE) analysis within a causal framework was employed to estimate treatment effects on the residuals, uncovering more intricate and complex causal relationships. The study demonstrated that CF method provides more stable ATE estimates. An analysis of the distribution of the treatment effects revealed a diverse range of impacts, indicating both positive and negative outcomes. Significant variability in treatment effects was evident from heterogeneous testing results. Noteworthy outcomes from treating freeway exits were observed in areas with an Average Annual Daily Traffic (AADT) ranging from 12,000 to 28,000 vehicles per day, average speeds of 95 km/h and above, two or four lanes on each side, and an exit-only ramp configuration. These findings contribute to valuable technical insights for selecting and evaluating safety enhancement strategies on freeways.