Based on my prior knowledge, the credit_history feature can potentially have an impact on whether a person receives credit or not. However, without any additional information about the data or the specific task at hand, it is difficult to ascertain the exact relationship between the feature and the target.

To create the dictionary, I will assume the following possible relationships between the credit_history feature and the target variable:

1. If a person has a "critical/other existing credit" or "delayed previously" credit history, they might be less likely to receive credit. 
2. If a person has an "existing paid" or "no credits/all paid" credit history, they might be more likely to receive credit. 
3. If a person has an "all paid" credit history, it might indicate a positive credit history and increase their chances of receiving credit. 

However, it's important to note that these assumptions are based on general knowledge and may not hold true for every specific case. Without further information or data analysis, it is hard to determine the exact relationship between the credit_history feature and the target variable.

Based on these assumptions, the generated dictionary would look like:

```json
{
	"yes": ["existing paid", "no credits/all paid", "all paid"],
	"no": ["critical/other existing credit", "delayed previously"]
}
```