# Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
## 3 Experiments
### Experimental Analysis  
In this section, we go beyond the correlation matrix. By leveraging causal discovery with reinforcement learning, we uncover the underlying causal mechanisms driving delivery risks. Causal discovery allows us to establish the directionality between variables, distinguishing between factors that are merely correlated and those that are true drivers of delays. The final causal graph in Figure 6 illustrates the intricate relationships between various features and their impact on 'Late_delivery_risk' (\(X_{7}\)), providing a clear map of how different variables contribute to the likelihood of a late delivery. Notably, the shipping mode (\(X_{16}\)) emerges as a key factor, exerting a strong direct influence on 'Days for shipping (real)' (\(X_{2}\)), with a causal strength of 9.24. This relationship, in turn, significantly affects both 'Days for shipment (scheduled)' (\(X_{3}\)) and directly influences 'Late_delivery_risk' (\(X_{7}\)), with strengths of 8.92 and 10.25, respectively. These results suggest that the choice of shipping mode triggers a cascade of effects, ultimately determining whether a delivery will be late. Figure 6: Causal structure for delivery risk. Additionally, 'Delivery Status' (\(X_{6}\)) has a direct and significant impact on 'Late_delivery_risk', with a causal strength of 10.66, indicating that certain delivery statuses are strong predictors of potential delays. The interconnectedness of these nodes shows that delays in actual shipping times not only increase the risk of late delivery directly but also indirectly affect other factors, such as scheduled shipping times, compounding the delay. At a more moderate level, 'Sales per customer' (\(X_{5}\)) and 'Order Item Product Price' (\(X_{12}\)) influence 'Late_delivery_risk' indirectly, with causal strengths of 1.10 and 1.12, respectively. While these variables have less impact, they suggest that the value of orders and customer segments may affect delivery priority or efficiency, thereby influencing the overall risk of delays. This combination of direct and indirect effects highlights the complexity of interactions where both logistical choices and customer-related factors converge to influence delivery outcomes. Figure 7 reveals additional patterns within the supply chain that, while not directly tied to 'Late_delivery_risk', provide valuable business insights. The analysis shows that both 'Order Item Product Price' (\(X_{11}\)) and 'Order Item Quantity' (\(X_{14}\)) moderately influence 'Benefit per Order' (\(X_{4}\)), each with a causal strength of 1.28. This suggests that higher product prices and larger order quantities contribute to increased profitability per order. More importantly, 'Order Item Profit Ratio' (\(X_{13}\)) exerts a stronger influence on 'Benefit per Order', with a causal strength of 2.20, emphasizing the pivotal role of profit margins in driving overall order profitability. These results underscore the importance of optimizing pricing strategies and profit margins to maximize financial performance within the supply chain. **Comparative Analysis with Existing Literature**: Our causal analysis identified **shipment mode** and **delivery status** as key drivers with significant causal strengths impacting delivery risks. This finding aligns with previous research by [PERSON] and [PERSON] [17; 18], which highlighted shipment mode as a critical factor in determining on-time delivery performance. However, while prior studies often established correlations between shipment mode and delays, they lacked insights into the causal direction and strength of these relationships. Our approach quantifies the causal strength (e.g., a causal strength of 9.24 from shipment mode to actual shipping days), illustrating not just association but the magnitude  
Figure 7: Causal structure for benefits per order. of impact that different shipment modes exert on delivery timelines.