Optimizing Causal Objective Functions - Algorithms and Complexity

Optimizing Causal Objective Functions - Algorithms and Complexity

UCLA Automated Reasoning Group via YouTube Direct link

Worlds

10 of 27

10 of 27

Worlds

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Optimizing Causal Objective Functions - Algorithms and Complexity

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  1. 1 Causal objective functions: What and why?
  2. 2 The causal hierarchy
  3. 3 Structural causal models SCM
  4. 4 The identifiability/learning dimension
  5. 5 Unit selection: The work of Li & Pearl
  6. 6 The algorithmic dimension of unit selection optimization
  7. 7 Examples of causal objective functions
  8. 8 Syntax and semantics of causal objective functions
  9. 9 Sub-models
  10. 10 Worlds
  11. 11 Events associational, interventional, counterfactual
  12. 12 Satisfaction of an event by a world
  13. 13 Example of satisfaction
  14. 14 Probability of events associational, interventional, counterfactual
  15. 15 Generalized events: conjunctive, disjunctive and negated
  16. 16 Variable elimination for computing associational queries
  17. 17 MAR marginals
  18. 18 MAP maximum a posteriori hypothesis
  19. 19 Treewidth & elimination orders
  20. 20 Optimizing causal objective functions
  21. 21 Triplet models: evaluating counterfactual queries
  22. 22 Objective models: optimizing the causal objective function using Reverse-MAP
  23. 23 Reverse-MAP: Relation to MAP, complexity class of Reverse-MAP and unit selection
  24. 24 Reverse-MAP: Algorithm, complexity bound, experiments
  25. 25 Elimination orders and treewidth of parallel worlds twin, triplet, .., models
  26. 26 Causal treewidth
  27. 27 Main messages

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