Causal Effect Identification from Multiple Incomplete Data Sources

Causal Effect Identification from Multiple Incomplete Data Sources

Alan Turing Institute via YouTube Direct link

Intro

1 of 26

1 of 26

Intro

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Classroom Contents

Causal Effect Identification from Multiple Incomplete Data Sources

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  1. 1 Intro
  2. 2 Starting point
  3. 3 The data-fusion problem
  4. 4 Identifiability problems in causal inference
  5. 5 The general identifiability problem
  6. 6 Motivation for a search-based approach
  7. 7 Search over the rules of do-calculus
  8. 8 Example on applying do-search
  9. 9 Missing data in causal inference
  10. 10 Example: case-control design.
  11. 11 Identifiability problems reassessed (with missing data)
  12. 12 Context-specific Independence
  13. 13 Alternative Representations for CSI
  14. 14 Labeled Directed Acyclic Graphs
  15. 15 Example on Context-specific DAGS
  16. 16 CSI-separation Example
  17. 17 Causal Effect Identification in LDAGS
  18. 18 Interventions in LDAGS
  19. 19 Complexity of the Decision Problem
  20. 20 Search over the rules of CSI-calculus
  21. 21 Search Example
  22. 22 Derivation of the Example
  23. 23 A Curious Example
  24. 24 Some Properties of the Search
  25. 25 Open Problems and Possible Future Work
  26. 26 References I

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