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