Assignment -

1. When A causes B, B causes C, and there are no other causal relationships, what implication do these relationships have for the conditional probabilities?

2. What are the steps in conducting a root-cause analysis?

3. How can you validate the root-cause analysis? List and discuss specific ways that assumptions in root-cause analysis can be verified.

4. If a root-cause analysis of wrong-site surgery exceeds by several folds the observed frequency of wrong-site surgery, what implications does this have for the analysis?

5. What are serial, diverging, and converging structures, and which ones imply conditional independence?

6. What is meant by reverse prediction and why is that more useful than directly predicting a rare event?

7. In the example of root-cause analysis of wrong-site surgery given in the lecture, what is the probability of finding that the patient was responsible? If in the past you have reviewed 100 wrong-site surgeries and found that 5 percent of them were because of patient misinformation, what is the implication of this finding for the root cause analysis?

8. Using Netica software, create Bayesian network for wrong-site surgery. Use the network structure and probabilities exactly as described in the lecture. Include screenshots of the network and upload Netica file.