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Intro
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Classroom Contents
Orthogonal Statistical Learning
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- 1 Intro
- 2 The Ease of Use of Machine Learning
- 3 The Ease of (mis)Use of Machine Learning
- 4 Making CausalML Accessible to Every Decision-Maker
- 5 Automated Outlier Removal
- 6 Desiderata for a good robust estimator
- 7 Estimation via Moment Conditions
- 8 Generalized Method of Moments
- 9 Our Results
- 10 Two-part theorem
- 11 Algorithm and Key Ingredients
- 12 Example Application Theorem: IV Regression
- 13 Experiments
- 14 Application: NLSYM
- 15 Method of Moments with Nuisance Functions
- 16 De-biased moment
- 17 The Adversarial Approach
- 18 The Direct Loss Approach
- 19 Formal Theorem
- 20 Neural Network and Forest Heuristic Improvements
- 21 Multi-Tasking
- 22 Application: 401k
- 23 Application: Gasoline Demand
- 24 Beyond Linear Moments
