1. Step 2: Make a Causal Model Farrokh Alemi Ph.D. This research was funded by Grant RO1 HL 084767 from the National Heart Blood and Lung Institute.

2. Step 2: Make a Causal Model This lecture continues from the lecture on making a list of causes

3. Step 2: Make a Model from Your List

4. Put Each Cause in a Node

5. Put the Effect to the Right

6. Connect Causes to Effect

7. Put in Constraints

8. Equivalent Probability Model

9. Each Node a Function of Nodes Linking to It

10. Combination of Multiple Causes p(Exercise | Ready to bike, Ready to shower at gym, Sleep early)= high p(Exercise | Ready to bike, Ready to shower at gym, Slept late)= Between low and high p(Exercise | Ready to bike, No plans to shower at gym, Sleep early)= Between low and high p(Exercise | No plans to bike, Ready to shower at gym, Sleep early)= Between low and high p(Exercise | Ready to bike, No plans to shower at gym, Slept late)= Between low and high p(Exercise | No plans to bike, Ready to shower at gym, Slept late)= Between low and high p(Exercise | No plans to bike, No plans to shower at gym, Slept late)= Low

11. Out of Sight, Out of Mind Breakfast time

12. Probabilistic Dependence

17. Probabilistic Independence

19. Serial Nodes: Root Causes

20. Multiple Causes

21. Causal model Probability distribution Test against data Probabilities Can Be Tested Against Data

22. Lecture Continues Step 3: Thought Experiments