A New Central Limit Theorem for Augmented IPW Estimator in High Dimensions

A New Central Limit Theorem for Augmented IPW Estimator in High Dimensions

Harvard CMSA via YouTube Direct link

Robustness to assumptions: Beyond independence

18 of 22

18 of 22

Robustness to assumptions: Beyond independence

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A New Central Limit Theorem for Augmented IPW Estimator in High Dimensions

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  1. 1 Intro
  2. 2 Outline
  3. 3 Causal inference from observational studies
  4. 4 Conditions for ATE identification
  5. 5 ATE estimation: A well-studied problem
  6. 6 Properties
  7. 7 Extensions to high dimensions
  8. 8 Issue: Fails to capture certain phenomena
  9. 9 Recall the structure
  10. 10 An important consideration: Cross-fitting
  11. 11 Our formal setting
  12. 12 Across diverse disciplines
  13. 13 The main result
  14. 14 Comparison with classical variance
  15. 15 Takeaway 1: Illustration
  16. 16 Theory vs empirical
  17. 17 Effects of regularization
  18. 18 Robustness to assumptions: Beyond independence
  19. 19 The theoretical workhorses
  20. 20 Quick peek into Cavity Method in our setting
  21. 21 Causal inference uncovers novel challenges
  22. 22 Wrapping Up

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