# Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
## 3 Experiments
### Experimental Data  
#### 3.1.1 DataCo Global  
We first describe the experimental setup used to evaluate the model. This section outlines the dataset, performance metrics, and baseline models used as benchmarks for comparison. For this analysis, we employed the DataCo Global Supply Chain dataset from Kaggle, which is designed for analyzing various aspects of supply chain management. The dataset contains 180,520 samples and 53 features. Our goal is to identify the root causes of late delivery risks by applying our causal discovery method via reinforcement learning with causal strength calculation.  
In the preprocessing stage, we manually filtered out variables deemed less relevant to delivery risk, such as \"Order Date\" and \"Order City\", which had minimal influence on the analysis. This step reduced unnecessary noise and allowed us to concentrate on the most important variables. After this initial filtering, we addressed multicollinearity, which occurs when two or more features are highly correlated, potentially skewing the model by introducing redundant information. To resolve this, we computed the correlation matrix and removed features with correlation coefficients above 0.95, retaining only one representative feature from each correlated group. This step streamlined the model and improved its interpretability by ensuring that each feature contributed unique information.  
Next, categorical variables such as \"Shipment Mode\" and \"Customer Segment\" were converted into numerical values using integer encoding, as most machine learning algorithms require numerical inputs. After encoding, we addressed missing values. Features with a large proportion of missing data (over 40%) and those irrelevant to the outcome were excluded to improve data quality and reduce bias in the model. Finally, we selected 16 key features based on their relevance to the delivery risk problem, ensuring a balanced and optimized input dataset for modeling. These features, including shipment time and delivery status, were directly related to late deliveries. The remaining features are listed in Table 3, with detailed descriptions provided in Appendix A.  
This preprocessing approach helps prevent overfitting, enhances model generalization, and simplifies interpretation by focusing on the most relevant data. Additionally, by removing irrelevant or redundant variables, we improve computational efficiency and ensure the model is better equipped to handle real-world scenarios, providing reasonable explanations for the root causes of late deliveries.  
In the next subsection, we will conduct a comprehensive exploratory data analysis (EDA) on the dataset. This analysis will involve examining the data in detail to uncover underlying patterns, relationships, and trends. Using various visualization techniques  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Denotation** & **Variable** & **Denotation** & **Variable** \\ \hline \(X_{1}\) & Type & \(X_{9}\) & Latitude \\ \hline \(X_{2}\) & Days for shipping (real) & \(X_{10}\) & Longitude \\ \hline \(X_{3}\) & Days for shipment (scheduled) & \(X_{11}\) & Order Item Discount Rate \\ \hline \(X_{4}\) & Benefit per order & \(X_{12}\) & Order Item Product Price \\ \hline \(X_{5}\) & Sales per customer & \(X_{13}\) & Order Item Profit Ratio \\ \hline \(X_{6}\) & Delivery Status & \(X_{14}\) & Order Item Quantity \\ \hline \(X_{7}\) & Late\_delivery\_risk & \(X_{15}\) & Order Status \\ \hline \(X_{8}\) & Customer Segment & \(X_{16}\) & Shipping Mode \\ \hline \hline \end{tabular}
\end{table}
Table 3: Denotations and their corresponding variables.  
and statistical summaries, we aim to identify key insights that can inform our modeling approach and provide a deeper understanding of the factors contributing to late delivery risks. These insights will be critical in guiding feature selection and improving the accuracy and interpretability of the model.