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Step 2: Make a Causal Model

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

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Step 2: Make a Causal Model

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  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 • Real world relationships • Visual network of causes & effects • Probability distribution

  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

  13. Probabilistic Dependence

  14. Probabilistic Dependence

  15. Probabilistic Dependence

  16. Probabilistic Dependence

  17. Probabilistic Independence • If we knew … • P(A|B) = P(A)

  18. Probabilistic Independence

  19. Serial Nodes: Root Causes

  20. Multiple Causes

  21. Probabilities Can Be Tested Against Data

  22. Lecture Continues Step 3: Thought Experiments

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