Safety has been one of the major concerns by the traffic police in traffic management. A favored practice to enhance traffic safety is to educate drivers and raise their safety awareness, so as to reduce the occurrence of traffic accidents in general. However, human power is limited in carrying out the education program, and challenges remain in filtering the most proper group of drivers to receive such education as well as in assessing the effectiveness of the program. In this paper, we view traffic safety education as an intervention from the perspective of causal inference, and we address the driver recipient selection (DRS) problem as a combination of uplift modeling and optimization. In uplift modeling, we identify that the confounding bias is present in historical accident data, and hence we adapt the uplift model via inverse propensity scoring (IPS) to eliminate the confounding bias. Experiments on both synthetic and real-world datasets show that our adapted uplift model increases the Area Under the Unconfounded Uplift Curve (AUUUC) by up to 46%, and our proposed DRS strategy can further reduce the overall monthly accident rate by 3.4% absolutely than the existing strategy.